ABSTRACT A fertile complementarity exists between scatter search (SS) and particle swarm optimiza... more ABSTRACT A fertile complementarity exists between scatter search (SS) and particle swarm optimization (PSO). Shared and contrasting principles underlying these methods provide a fertile basis for combining them to create a hybrid method. We identify a specific hybrid, scatter PSO, giving rise to two variants that prove more effective than the constriction factor model of PSO. Applied to finding global minima for continuous nonlinear functions, scatter PSO not only is able to obtain better solutions to a widely used set of benchmark functions, but also proves more robust under a variety of experimental conditions.
Page 1. '1' 1994 Intemational Symposium on Speech, Image Processing and Neural Networks... more Page 1. '1' 1994 Intemational Symposium on Speech, Image Processing and Neural Networks, 13-16 April 1994, Hong Kong A NEW METHOD FOR MULTILEVEL THXESHOLDIKG USING SYMMETRY AND DUALITY OF THE HISTOGR'M ...
We propose a new approach for detecting ellipses. The approach is based on the geometrical proper... more We propose a new approach for detecting ellipses. The approach is based on the geometrical property that five points on an ellipse can determine the parameters of the ellipse, and the symmetry of ellipses is used to obtain these points. Using symmetry, we classify the edge points in an input image into several subimages. Ellipses with different symmetric axes will lie in different subimages. In each subimage, symmetry is applied again to obtain those sets of five points that possibly lie on the same ellipse. Finally, using geometrical properties and the Hough transform, we extract all ellipses in an input image successfully. The proposed method can detectpartially occluded ellipses. Experimental results show that the proposed method is fast and robust.
The previously published optimal thresholding techniques based on some objective functions are ve... more The previously published optimal thresholding techniques based on some objective functions are very efficient in the bi-level thresholding case, but they are impractical when extended to multilevel thresholding. The reason for this is their computational complexity ...
Organizing knowledge workers for specific tasks in a software development process is critical for... more Organizing knowledge workers for specific tasks in a software development process is critical for the success of software projects. Assigning workforce in software projects represents a dynamic and complex problem that concerns the utilization of cross-trained knowledge workers who possess different productivities and error tendencies in coding and defect correction. This complexity is further compounded when the development process follows a software release life cycle and involves major releases of alpha, beta, and final versions in the context of iterative software development. We study this knowledge workforce problem from three essential project management perspectives: (1) timelinessobtaining shortest development time; (2) effectivenesssatisfying budget constraint; and (3) efficiencyachieving high workforce utilization. We explore ideal workforce composites with two strategic focuses on productivity and quality and with different scenarios of workload ratios. An analytical model is formulated and a meta-heuristic approach based on particle swarm optimization is used to derive solutions in a simulation experiment. Our findings suggest that forming an ideal workforce composite is a non-trivial task and task assignments with divergent focuses for software projects under different workload scenarios require different planning strategies. Practical implications are drawn from our findings to provide insight on effectively planning workforce for software projects with specific goals and considerations.
In a distributed system, a number of application tasks may need to be assigned to different proce... more In a distributed system, a number of application tasks may need to be assigned to different processors such that the system cost is minimized and the constraints with limited resource are satisfied. Most of the existing formulations for this problem have been found to be NP-complete, and thus finding the exact solutions is computationally intractable for large-scaled problems. This paper
The Open Artificial Intelligence Journal, Feb 27, 2009
We consider the assignment of program tasks to processors in distributed computing systems such t... more We consider the assignment of program tasks to processors in distributed computing systems such that system cost is minimized and resource constraints are satisfied. Several formulations for this task assignment problem (TAP) have been proposed in the literature. Most of these TAP formulations, however, are NP-complete and thus finding exact solutions is computationally intractable. Recently, some approximation methods like simulated annealing have been proposed, and simulation results exhibited the potential to solve the TAP using metaheuristics. In order to better understand the strengths and weaknesses of various metaheuristics applied to the TAP, we first propose two alternative metaheuristics-one using genetic algorithm and the other reinforcement learning algorithm-as well as their implementation details. Extensive computational evidences of the two heuristic algorithms against that of simulated annealing are presented, compared and discussed. Based on these experimental results, a hybrid strategy employing both metaheuristics is then proposed in order to solve the TAP more effectively and efficiently.
In a distributed computing system (DCS), we need to allocate a number of modules to different pro... more In a distributed computing system (DCS), we need to allocate a number of modules to different processors for execution. It is desired to maximize the processor synergism in order to achieve various objectives, such as throughput maximization, reliability maximization, and cost minimization. There may also exist a set of system constraints related to memory and communication link capacity. The considered problem has been shown to be NP-hard. Most existing approaches for task allocation deal with a single objective only. This paper presents a multi-objective task allocation algorithm with presence of system constraints. The algorithm is based on the particle swarm optimization which is a new metaheuristic and has delivered many successful applications. We further devise a hybrid strategy for expediting the convergence process. We assess our algorithm by comparing to a genetic algorithm and a mathematical programming approach. The experimental results manifest that the proposed algorithm performs the best under different problem scales, task interaction densities, and network topologies.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefl... more Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.
The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribut... more The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. In contrast to classic evolutionary algorithms (EAs), EDA framework is flexible and is able to handle inter variable dependence, which usually imposes difficulties on classic EAs. The success of EDA relies on effective and efficient building of the probability model. This paper facilitates EDA from the adaptive memory programming (AMP) domain which has developed several improved forms of EAs using the Cyber-EA framework. The experimental result on benchmark TSP instances supports our anticipation that the AMP strategies can enhance the performance of classic EDA by deriving a better approximation for the true distribution of the target solutions.
Wireless communication is a central technology to many applications such as wireless TV, radio br... more Wireless communication is a central technology to many applications such as wireless TV, radio broadcasting, global positioning, satellite-based cellular systems, mobile telephony, wireless LAN, to name a few. The research and development of wireless products have also bloomed the wireless communication applications to a new era. However, the available bandwidth does not grow as fast as the exploding demands from the consumer markets, so we need an algorithm to effectively and repetitively assign the available channels to multiple demanding cells such that no electromagnetic interference is induced. The aim of the channel assignment problem (CAP) is to minimize the span, the spectrum between the maximum and minimum used frequency, of allocated channels with an associated assignment that satisfies the bandwidth demands without incurring electromagnetic interference among them. The CAP can be polynomially reduced to the graph-coloring problem which has been known to be NP-hard. This means the derivation of the exact solution to the CAP in the general case is computationally prohibitive. Most existing methods for tackling CAP are based on three approaches, namely, mathematical programming, heuristics, and metaheuristics. The mathematical programming techniques such as integer linear programming (Janssen & Kilakos, 1999; Mathar & Schmeink, 2002) and branch-and-bound (Tcha et al., 1997) are efficient in finding the exact solutions, however, they are limited to the application of small-sized problems only. Heuristics such as ordering technique (Sivarajan et al., 2000) and sequential packing technique (Sung & Wong, 1997) use a heuristic function for determining the order or packing of radio cells to allocate channels. These methods can quickly obtain a feasible solution even for a large problem but the solution quality varies a lot with the instances of the problem. Alternatively, more and more CAP researchers are attracted by the promising results on some applications using metaheuristics including genetic algorithms (
In a distributed computing system, a number of program modules may need to be allocated to differ... more In a distributed computing system, a number of program modules may need to be allocated to different processors such that the reliability of executing successfully these modules is maximized and the constraints with limited resources are satisfied. The problem of finding an optimal task allocation with maximum system reliability has been shown to be NP-hard; thus, existing approaches to finding exact solutions are limited to the use in problems of small size. This paper presents a hybrid particle swarm optimization (HPSO) algorithm for finding the near-optimal task allocation within reasonable time. The experimental results show that the HPSO is robust against different problem size, task interaction density, and network topology. The proposed method is also more effective and efficient than a genetic algorithm for the test-cases studied. The convergence and the worst-case characteristics of the HPSO are addressed using both theoretical and empirical analysis.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
The ascending of quantity of CO 2 emissions is the main factor contributing the global warming wh... more The ascending of quantity of CO 2 emissions is the main factor contributing the global warming which results in extremely abnormal weather and causes disaster damages. Due to intensive CO 2 pollutants produced by classic energy sources such as fossil fuels, practitioners and researchers pay increasing attentions on the renewable energy production such as wind power. Optimal wind turbine placement problem is to find the optimal number and placement location of wind turbines in a wind farm against the wake effect. The efficiency of wind power production does not necessarily grows with an increasing number of installed wind turbines. This paper presents a hyper-heuristic framework combining several lower-level heuristics with an artificial bee colony algorithm and a simulated annealing technique to construct an optimal wind turbine placement considering wake effect influence. Finally, we compare our approach with existing works in the literature. The experimental results show that our approach produces the wind power with a lower cost of energy.
Wind farm micro-siting is to determine the optimal placement for the wind turbines such that the ... more Wind farm micro-siting is to determine the optimal placement for the wind turbines such that the cost of energy (COE) is minimal. The problem is clearly a long-term decision one, once the micro-siting was constructed, it is extremely costly to reconfigure the layout. Long-term electricity demand forecasting is a necessity for formation of governmental energy policy. We anticipate that the two problems should be considered simultaneously to create potential benefits because they have resembling properties and close supply-and-demand relationship. This paper proposes a demand-aware micro-siting system to COE minimization. The system is a holistic integration of long-term electricity demand forecasting and optimal micro-siting. A case study in central Taiwan area is conducted to validate the feasibility of the proposed system.
Resource management is the effective deployment for an organization's resources. It deals with cl... more Resource management is the effective deployment for an organization's resources. It deals with classification, allocation, stocking, processing, storage, and valuation for the shared resources when they are needed. Among many managerial tasks, the constrained resource allocation problem has challenging many practitioners involved in manufacturing operations. This problem seeks to find an optimal allocation of a limited amount of resource to a number of manufacturing activities for optimizing organization's objective subject to the resource constraints. Most existing methods use mathematical programming techniques, but they are defaced in deriving exact solutions for large-scale problems with reasonable time. A viable alternative is to use swam algorithms which can obtain approximate solutions to real-world intractable problems. This paper presents two swarm algorithms embodying the adaptive resource bound technique for conquering the constrained nonlinear resource allocation problem. Experimental results manifest that the proposed methods are more effective and efficient than a genetic algorithm-based approach. The convergence behavior of the proposed methods is analyzed by observing the variations of population entropy. Finally, a worst-case analysis is conducted to provide a reliable performance guarantee.
In this study, an exemplar-based classifier was developed to predict which decision strategy may ... more In this study, an exemplar-based classifier was developed to predict which decision strategy may underlie an empirical ocular search behavior. Our rationale was mainly inspired by the exemplar-based models of categorization; that is, different decision strategies are conceived as different concepts, with the exemplar referring to the sequence of empirical fixations on decision information during a decision process. In order to ascertain the best exemplar of each strategy for our classifier, the Tabu search algorithm was applied. An eyetracking based experiment was conducted to collect fixation data for training and validation. Our result showed that the classifier has significant accuracy in identifying underlying strategies, achieving an average hit-ratio of 76%. This indicated to us that the integration of the exemplar classifier with fixation data has certain applicable value for leveraging the adaptability of DSSs. Our result also has some important implications for the direction and methodology of behavioral decision research
ABSTRACT A fertile complementarity exists between scatter search (SS) and particle swarm optimiza... more ABSTRACT A fertile complementarity exists between scatter search (SS) and particle swarm optimization (PSO). Shared and contrasting principles underlying these methods provide a fertile basis for combining them to create a hybrid method. We identify a specific hybrid, scatter PSO, giving rise to two variants that prove more effective than the constriction factor model of PSO. Applied to finding global minima for continuous nonlinear functions, scatter PSO not only is able to obtain better solutions to a widely used set of benchmark functions, but also proves more robust under a variety of experimental conditions.
Page 1. '1' 1994 Intemational Symposium on Speech, Image Processing and Neural Networks... more Page 1. '1' 1994 Intemational Symposium on Speech, Image Processing and Neural Networks, 13-16 April 1994, Hong Kong A NEW METHOD FOR MULTILEVEL THXESHOLDIKG USING SYMMETRY AND DUALITY OF THE HISTOGR'M ...
We propose a new approach for detecting ellipses. The approach is based on the geometrical proper... more We propose a new approach for detecting ellipses. The approach is based on the geometrical property that five points on an ellipse can determine the parameters of the ellipse, and the symmetry of ellipses is used to obtain these points. Using symmetry, we classify the edge points in an input image into several subimages. Ellipses with different symmetric axes will lie in different subimages. In each subimage, symmetry is applied again to obtain those sets of five points that possibly lie on the same ellipse. Finally, using geometrical properties and the Hough transform, we extract all ellipses in an input image successfully. The proposed method can detectpartially occluded ellipses. Experimental results show that the proposed method is fast and robust.
The previously published optimal thresholding techniques based on some objective functions are ve... more The previously published optimal thresholding techniques based on some objective functions are very efficient in the bi-level thresholding case, but they are impractical when extended to multilevel thresholding. The reason for this is their computational complexity ...
Organizing knowledge workers for specific tasks in a software development process is critical for... more Organizing knowledge workers for specific tasks in a software development process is critical for the success of software projects. Assigning workforce in software projects represents a dynamic and complex problem that concerns the utilization of cross-trained knowledge workers who possess different productivities and error tendencies in coding and defect correction. This complexity is further compounded when the development process follows a software release life cycle and involves major releases of alpha, beta, and final versions in the context of iterative software development. We study this knowledge workforce problem from three essential project management perspectives: (1) timelinessobtaining shortest development time; (2) effectivenesssatisfying budget constraint; and (3) efficiencyachieving high workforce utilization. We explore ideal workforce composites with two strategic focuses on productivity and quality and with different scenarios of workload ratios. An analytical model is formulated and a meta-heuristic approach based on particle swarm optimization is used to derive solutions in a simulation experiment. Our findings suggest that forming an ideal workforce composite is a non-trivial task and task assignments with divergent focuses for software projects under different workload scenarios require different planning strategies. Practical implications are drawn from our findings to provide insight on effectively planning workforce for software projects with specific goals and considerations.
In a distributed system, a number of application tasks may need to be assigned to different proce... more In a distributed system, a number of application tasks may need to be assigned to different processors such that the system cost is minimized and the constraints with limited resource are satisfied. Most of the existing formulations for this problem have been found to be NP-complete, and thus finding the exact solutions is computationally intractable for large-scaled problems. This paper
The Open Artificial Intelligence Journal, Feb 27, 2009
We consider the assignment of program tasks to processors in distributed computing systems such t... more We consider the assignment of program tasks to processors in distributed computing systems such that system cost is minimized and resource constraints are satisfied. Several formulations for this task assignment problem (TAP) have been proposed in the literature. Most of these TAP formulations, however, are NP-complete and thus finding exact solutions is computationally intractable. Recently, some approximation methods like simulated annealing have been proposed, and simulation results exhibited the potential to solve the TAP using metaheuristics. In order to better understand the strengths and weaknesses of various metaheuristics applied to the TAP, we first propose two alternative metaheuristics-one using genetic algorithm and the other reinforcement learning algorithm-as well as their implementation details. Extensive computational evidences of the two heuristic algorithms against that of simulated annealing are presented, compared and discussed. Based on these experimental results, a hybrid strategy employing both metaheuristics is then proposed in order to solve the TAP more effectively and efficiently.
In a distributed computing system (DCS), we need to allocate a number of modules to different pro... more In a distributed computing system (DCS), we need to allocate a number of modules to different processors for execution. It is desired to maximize the processor synergism in order to achieve various objectives, such as throughput maximization, reliability maximization, and cost minimization. There may also exist a set of system constraints related to memory and communication link capacity. The considered problem has been shown to be NP-hard. Most existing approaches for task allocation deal with a single objective only. This paper presents a multi-objective task allocation algorithm with presence of system constraints. The algorithm is based on the particle swarm optimization which is a new metaheuristic and has delivered many successful applications. We further devise a hybrid strategy for expediting the convergence process. We assess our algorithm by comparing to a genetic algorithm and a mathematical programming approach. The experimental results manifest that the proposed algorithm performs the best under different problem scales, task interaction densities, and network topologies.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefl... more Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.
The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribut... more The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. In contrast to classic evolutionary algorithms (EAs), EDA framework is flexible and is able to handle inter variable dependence, which usually imposes difficulties on classic EAs. The success of EDA relies on effective and efficient building of the probability model. This paper facilitates EDA from the adaptive memory programming (AMP) domain which has developed several improved forms of EAs using the Cyber-EA framework. The experimental result on benchmark TSP instances supports our anticipation that the AMP strategies can enhance the performance of classic EDA by deriving a better approximation for the true distribution of the target solutions.
Wireless communication is a central technology to many applications such as wireless TV, radio br... more Wireless communication is a central technology to many applications such as wireless TV, radio broadcasting, global positioning, satellite-based cellular systems, mobile telephony, wireless LAN, to name a few. The research and development of wireless products have also bloomed the wireless communication applications to a new era. However, the available bandwidth does not grow as fast as the exploding demands from the consumer markets, so we need an algorithm to effectively and repetitively assign the available channels to multiple demanding cells such that no electromagnetic interference is induced. The aim of the channel assignment problem (CAP) is to minimize the span, the spectrum between the maximum and minimum used frequency, of allocated channels with an associated assignment that satisfies the bandwidth demands without incurring electromagnetic interference among them. The CAP can be polynomially reduced to the graph-coloring problem which has been known to be NP-hard. This means the derivation of the exact solution to the CAP in the general case is computationally prohibitive. Most existing methods for tackling CAP are based on three approaches, namely, mathematical programming, heuristics, and metaheuristics. The mathematical programming techniques such as integer linear programming (Janssen & Kilakos, 1999; Mathar & Schmeink, 2002) and branch-and-bound (Tcha et al., 1997) are efficient in finding the exact solutions, however, they are limited to the application of small-sized problems only. Heuristics such as ordering technique (Sivarajan et al., 2000) and sequential packing technique (Sung & Wong, 1997) use a heuristic function for determining the order or packing of radio cells to allocate channels. These methods can quickly obtain a feasible solution even for a large problem but the solution quality varies a lot with the instances of the problem. Alternatively, more and more CAP researchers are attracted by the promising results on some applications using metaheuristics including genetic algorithms (
In a distributed computing system, a number of program modules may need to be allocated to differ... more In a distributed computing system, a number of program modules may need to be allocated to different processors such that the reliability of executing successfully these modules is maximized and the constraints with limited resources are satisfied. The problem of finding an optimal task allocation with maximum system reliability has been shown to be NP-hard; thus, existing approaches to finding exact solutions are limited to the use in problems of small size. This paper presents a hybrid particle swarm optimization (HPSO) algorithm for finding the near-optimal task allocation within reasonable time. The experimental results show that the HPSO is robust against different problem size, task interaction density, and network topology. The proposed method is also more effective and efficient than a genetic algorithm for the test-cases studied. The convergence and the worst-case characteristics of the HPSO are addressed using both theoretical and empirical analysis.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
The ascending of quantity of CO 2 emissions is the main factor contributing the global warming wh... more The ascending of quantity of CO 2 emissions is the main factor contributing the global warming which results in extremely abnormal weather and causes disaster damages. Due to intensive CO 2 pollutants produced by classic energy sources such as fossil fuels, practitioners and researchers pay increasing attentions on the renewable energy production such as wind power. Optimal wind turbine placement problem is to find the optimal number and placement location of wind turbines in a wind farm against the wake effect. The efficiency of wind power production does not necessarily grows with an increasing number of installed wind turbines. This paper presents a hyper-heuristic framework combining several lower-level heuristics with an artificial bee colony algorithm and a simulated annealing technique to construct an optimal wind turbine placement considering wake effect influence. Finally, we compare our approach with existing works in the literature. The experimental results show that our approach produces the wind power with a lower cost of energy.
Wind farm micro-siting is to determine the optimal placement for the wind turbines such that the ... more Wind farm micro-siting is to determine the optimal placement for the wind turbines such that the cost of energy (COE) is minimal. The problem is clearly a long-term decision one, once the micro-siting was constructed, it is extremely costly to reconfigure the layout. Long-term electricity demand forecasting is a necessity for formation of governmental energy policy. We anticipate that the two problems should be considered simultaneously to create potential benefits because they have resembling properties and close supply-and-demand relationship. This paper proposes a demand-aware micro-siting system to COE minimization. The system is a holistic integration of long-term electricity demand forecasting and optimal micro-siting. A case study in central Taiwan area is conducted to validate the feasibility of the proposed system.
Resource management is the effective deployment for an organization's resources. It deals with cl... more Resource management is the effective deployment for an organization's resources. It deals with classification, allocation, stocking, processing, storage, and valuation for the shared resources when they are needed. Among many managerial tasks, the constrained resource allocation problem has challenging many practitioners involved in manufacturing operations. This problem seeks to find an optimal allocation of a limited amount of resource to a number of manufacturing activities for optimizing organization's objective subject to the resource constraints. Most existing methods use mathematical programming techniques, but they are defaced in deriving exact solutions for large-scale problems with reasonable time. A viable alternative is to use swam algorithms which can obtain approximate solutions to real-world intractable problems. This paper presents two swarm algorithms embodying the adaptive resource bound technique for conquering the constrained nonlinear resource allocation problem. Experimental results manifest that the proposed methods are more effective and efficient than a genetic algorithm-based approach. The convergence behavior of the proposed methods is analyzed by observing the variations of population entropy. Finally, a worst-case analysis is conducted to provide a reliable performance guarantee.
In this study, an exemplar-based classifier was developed to predict which decision strategy may ... more In this study, an exemplar-based classifier was developed to predict which decision strategy may underlie an empirical ocular search behavior. Our rationale was mainly inspired by the exemplar-based models of categorization; that is, different decision strategies are conceived as different concepts, with the exemplar referring to the sequence of empirical fixations on decision information during a decision process. In order to ascertain the best exemplar of each strategy for our classifier, the Tabu search algorithm was applied. An eyetracking based experiment was conducted to collect fixation data for training and validation. Our result showed that the classifier has significant accuracy in identifying underlying strategies, achieving an average hit-ratio of 76%. This indicated to us that the integration of the exemplar classifier with fixation data has certain applicable value for leveraging the adaptability of DSSs. Our result also has some important implications for the direction and methodology of behavioral decision research
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