Nature-Inspired Computing

Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.

An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences

From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.

Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.

Firefly algorithm (FA) was developed by Xin-She Yang in 2008 and it has 1 become an important tool for solving the hardest optimization problems in almost 2 all areas of optimization as well as engineering practice. The literature has expanded 3 significantly in the last few years. Various FA variants have been developed to suit 4 different applications. This chapter provides a brief review of this expanding and 5 state-of-the-art literature on this dynamic and rapidly evolving domain of swarm 6 intelligence. 7 Keywords Firefly algorithm · Discrete firefly algorithm · Nature-inspired algorithm ·

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.

Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete

Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.

Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is presented. To overcome the difficulties in the evaluations of probabilistic constraints, the reliable design space concept has been applied to convert the yielded stochastic constrained optimization problem from the RBDO formulation into a deterministic constrained optimization problem. In addition, the constraint handling technique has also been introduced to the dBA so that the algorithm can solve constrained optimization problem effectively. The new method has been applied to several engineering problems and the results show that the new method can solve different varieties of RBDO problems efficiently. In fact, the obtained solutions are consistent with the best results in the literature.

Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering
algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers
from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global
optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental
results, FPAKM is better than FPA and K-Means.

The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm and to identify the possible effect of parameter settings on convergence, we have designed an updated model in terms of a dynamic matrix. Subsequently, we have used the stability theory of discrete-time dynamical systems to obtain the stable parameter ranges for the algorithm. Furthermore, we use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions.

Increasing demands upon current computer systems, along with technological changes, create a need for more flexible and adaptable systems. Natural systems provide many examples of the type of versatile system required. This paper reviews examples of nature-inspired computing, drawing inspiration from many different areas of living systems including evolution, ecology, development and behaviour. The implications for the future development of computing technology and applications are also discussed.

This presentation explains the fundamental ideas of the standard Flower Pollination Algorithm (FPA), which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube). An example of multi-objective flower pollination algorithm (MOPFA) is also given with link to the Matlab code.

Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this paper, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of mul-tobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis are highlighted and discussed.

Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC'2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm.

Combinatorial optimization problems, specially those that are NP-hard, are increasingly being dealt with by stochastic, metaheuristic approaches. Most recently developed metaheuristics are nature-inspired and they are often inspired by some special characteristics in evolution, ecological or biological systems. This chapter discusses how to go from a biological phenomenon such as the aggressive reproduction strategy of cuckoos to solve tough problems in the combinatorial search space. Key features and steps are highlighted, together with the discussions of further research topics.

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach. Transport networks are vital infrastructure of human society. Many networks are overloaded and often choked with traffic. Governments in most countries aim to ease congestion by imposing pollution charges 1,2 , tradable travel credits for congestion management 3 , parking permits distribution and trading 4. Such policies reduce the congestion to some extent, but they also lead to additional problems. For example, in the policy of tradable travel credits for congestion management, it might be complicated to make a decision on how to allocate the limited tradable travel credits to users, how many tradable travel credits should be provided, etc 5–7. One of the most promising solutions to this problem would be to design an efficient transit network for a transportation system. The efficient transit network is capable of maximizing throughout capacity and minimizing the overall costs 8–14. The network design problem (NDP) is one of the most challenging transport problems. The problem is defined as follows. Given a weighted graph G, we want to select such a subgraph in G that it satisfies the given point-to-point demand on a transportation and minimizes the overall costs of the transportation. In the past decades, various approaches have been presented to address this issue. The solutions can be divided into two categories: exact solutions 15–17 and heuristic solutions 18–21. Exact solution methods can deal with NDP in a rigorous manner. However, they are inefficient when dealing with large-scale real-world networks 18,22,23. Heuristic approaches, emerged in the past decades 24,25 , provide approximate yet efficient solutions. The heuristic approaches can tackle a real-world problems with a large number of design variables 26,27 and therefore these approaches are more popular than exact solutions 20,28–31. When we design a transportation network we should, ideally, make it fault tolerant, capable to cope with traffic accidents, terrorist attack, and emergency road maintenance. Fault tolerance and high performance attract higher building and maintenance costs. How to make a tradeoff between the overall

Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently.

Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random-key cuckoo search (RKCS) algorithm for solving the famous travelling salesman problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Lévy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other metaheuristic algorithms.

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright a b s t r a c t Software testing is an important but complex part of software development life cycle. The optimization of the software testing process is a major challenge, and the generation of the independent test paths remains unsatisfactory. In this paper, we present an approach based on metaheuristic firefly algorithm to generate optimal test paths. In order to optimize the test case paths, we use a modified firefly algorithm by defining appropriate objective function and introducing guidance matrix in traversing the graph. Our simulations and comparison show that the test paths generated are critical and optimal paths.

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

A new field titled 'Artificial Human Optimization' is introduced in this paper. All optimization methods which were proposed based on Artificial Humans will come under this new field. Less than 20 papers were published in this field so far. The goal of this paper is to introduce 'Artificial Human Optimization' and to show abstracts of papers published in this new field. The nick name given to this work is 'ENTREPRENEUR'.

A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics.

A supply chain is a system which moves products from a supplier to customers, which plays a very important role in all economic activities. This paper proposes a novel algorithm for a supply chain network design inspired by biological principles of nutrients' distribution in protoplasmic networks of slime mould Physarum polycephalum. The algorithm handles supply networks where capacity investments and product flows are decision variables, and the networks are required to satisfy product demands. Two features of the slime mould are adopted in our algorithm. The first is the continuity of flux during the iterative process, which is used in realtime updating of the costs associated with the supply links. The second feature is adaptivity. The supply chain can converge to an equilibrium state when costs are changed. Numerical examples are provided to illustrate the practicality and flexibility of the proposed method algorithm.

Global Optimization Techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization and other optimization techniques were used in literature to solve complex optimization problems. Many optimization algorithms were proposed in literature by taking the behavior of Birds, Ants, Fishes, Chromosomes etc. as inspiration. Recently, a new trend has begun in Evolutionary Computing Domain where optimization algorithms have been created by taking Human Behavior as inspiration. The focus of this paper is on optimization algorithms that were and are being created based on the behavior of Artificial Humans. In December 2016, a new field titled " Artificial Human Optimization " was proposed in literature. This paper is strongly meant to popularize " Artificial Human Optimization " field like never before by showing an Ocean of Opportunities that exists in this new and interesting area of research. A new field titled " Artificial Economics Optimization " is proposed at the end of paper.

— Amongst the multiple advantages and applications of remote sensing, one of the most important use is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this work, we propose a novel Bat Algorithm (BA) based clustering approach for solving crop type classification problems using a multi-spectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multi-spectral satellite image and one benchmark dataset from the UCI repository are used to demonstrate robustness of the proposed algorithm. The performance of the Bat Algorithm is compared with the traditional K-means and two other nature-inspired metaheuristic techniques, namely, Genetic Algorithm and Particle Swarm Optimization. From the results obtained, we can conclude that BA can be successfully applied to solve crop type classification problems.

The author proposed a new field titled “Artificial Human Optimization” in December 2016 [1]. He authored the following five articles in Artificial Human Optimization field:

1) Entrepreneur: Artificial Human Optimization. Transactions on Machine Learning and Artificial Intelligence, Volume 4 No 6 December (2016); pp: 64-70 [1]. 2) “CEO: Different Reviews on PhD in Artificial Intelligence”, Global Journal of Advanced Research, vol. 1, no.2, pp. 155-158, 2014 [2]. 3) “POSTDOC : The Human Optimization”, Computer Science & Information Technology (CS & IT), CSCP, pp. 183-187, 2013 [3]. 4) “Artificial Human Optimization – An Introduction”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 2, pp: 1-9, April 2018 [4]. 5) “An Ocean of Opportunities in Artificial Human Optimization Field”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 3, June 2018 [5].

The complete reviews of all the above papers are shown in this paper for the first time in Research Industry. This paper is a new kind of research paper where the focus is completely on the reviews obtained for a particular new field. This work is the extension of work in [2]. Similar to this paper, the article [2] is completely focused on reviews obtained. The difference between article [2] and this paper lies in the fact that in this paper reviews are shown for all the papers of the author in Artificial Human Optimization field whereas article [2] shows reviews of single paper in Artificial Human optimization field.

The cross-ambiguity function (CAF) relates to the correlation processing of signals in radar, sonar, and communication systems in the presence of delays and Doppler shifts. It is a commonly used tool in the analysis of signals in these systems when both delay and Doppler shifts are present. In this chapter, we aim to tackle the CAF synthesization problem such that the synthesized CAF approximates a desired CAF. A CAF synthesization problem is addressed by jointly designing a pair of waveforms using a metaheuristic approach based on the echolocation of bats. Through four examples, it is shown that such an approach can be used as an effective tool in synthesizing different types of CAFs.

Distributed computing opened a wide arena to massively scaled computing applications that were previously unimaginable. Load balancing is a key challenge in distributed network environment especially with dynamic load arrival. Load balancing problem can be considered as an optimization problem similar to scheduling problems. The success of nature inspired algorithms, particularly Ant Colony Optimization (ACO) on various optimization and scheduling problems has inspired to apply this approach on load balancing problem. Messor is one such approach based on AntNet which achieved limited success with some unaddressed problems. Extending the Messor's work, in this paper, we propose an ACO based approach to address load balancing problem. We use a combination ACO of and Artificial Neural Networks (ANNs). Swarm Intelligent techniques aid in identifying a suitable node for task transfers from overloaded network nodes to under loaded nodes and ANNs aid in directional decision making process that drives the ant search. Our approach is not limited to addressing the drawbacks of Messor but extends in providing a novel way of load balancing in a distributed environment. The proposed approach is evaluated against Messor with two types of load arrival strategies static and dynamic on different sized networks with 20, 60 and 100 nodes. The experimental results show that the proposed approach has resulted in achieving better performance.

The goal of this article is :

1) To popularize "Artificial Human Optimization" field
2) To show opportunities that exist in "Artificial Human Optimization" field.
3) To Design an optimization method based on Artificial Humans
4) To show reviews of papers in “Artificial Human Optimization” field
5) To make corrections to my previous work in “Artificial Human Optimization” field
6) To encourage researchers across the globe to work in “Artificial Human Optimization” field
7) To give Artificial Human Optimization award to researchers who contributed to this new field

This paper is dedicated to everyone who is interested in the Artificial Intelligence. John Henry Holland proposed Genetic Algorithm in the early 1970s. Ant Colony Optimization was proposed by Marco Dorigo in 1992. Particle Swarm Optimization was introduced by Kennedy and Eberhart in 1995. Storn and Price introduced Differential Evolution in 1996. K.M. Passino introduced Bacterial Foraging Optimization Algorithm in 2002. In 2003, X.L. Li proposed Artificial Fish Swarm Algorithm. Artificial Bee Colony algorithm was introduced by Karaboga in 2005. In the past, researchers have explored behavior of chromosomes, birds, fishes, ants, bacteria, bees and so on to create excellent optimization methods for solving complex optimization problems. In this paper, Satish Gajawada proposed The Human Optimization. Humans progressed like anything. They help each other. There are so many plus points in Humans. In fact all optimization algorithms based on other beings are created by Humans. There is so much to explore in behavior of Human for creating awesome optimization algorithms. Artificial Fishes, birds, ants, bees etc have solved optimization problems. Similarly, optimization method based on Humans is expected to solve complex problems. This paper sets the trend for all optimization algorithms that come in future based on Humans.

Nature in itself is the best example to solve problems in an efficient and effective manner. During the past few decades,
researchers are trying to create computational methods that can help human to solve complex problems. This may be achieved by transferring knowledge from natural systems to engineered systems.

New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which are based on concepts like Human Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", “Human Kindness Particle Swarm Optimization (HKPSO)", “Human Relaxation Particle Swarm Optimization (HRPSO)", “Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", “Human Thinking Particle Swarm Optimization (HTPSO)" and “Human Disease Particle Swarm Optimization (HDPSO)” are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm.

Agents need resources and protection from hostile influences, in both natural and artificial environments. This paper specifically considers social insects and software mobile agent systems, in order to understand how the need for mobility, resource acquisition and security can influence the design and operation of agent systems.  Evolution has honed the movement, resource-gathering and defensive skills of the social insects in a multitude of ways. The paper reviews some of these strategies and assesses whether they may provide useful insights into the design and implementation of mobile software agents. This process is necessary as current mobile agent systems have been handicapped by a lack of convincing security measures which has prevented their adoption in mainstream computing services.

This paper therefore examines the strategies for movement, resource acquisition and defence adopted by the social insects and considers how they may be applied to enhance the design of mobile agents.

As new areas of neural computing are trying to make at least one step beyond the definition of digital computing, the neural networks field, was developed around the idea of creating models of real neural systems. The key point is based on learning rather than programming. We designed the model we present in this paper, based on the
the needs introduced by new trends in neural computing. We created a model for information processing inside neurons. It is constructed, from a neural inside point of view, as a feedback system that controls the flow of information passing through the neuron. Processes of internal neural learning take place. We translated them as processes of
learning at ”molecular” level. Our work is disseminated into the construction of an algorithm of learning, that we introduce in the end of this paper