Swarm and Evolutionary Computation

Nature Inspired Computing (NIC) paradigms, namely, swarm intelligence, evolutionary computation and computational intelligence techniques are widely applied to develop versatile and adaptable systems and create computational methods that can assist human to resolve real world complex problems. It can be achieved by transferring knowledge from natural systems to engineering systems. The objective of this paper is to analyze the recent trends and advancement in the application of metaheuristic algorithms to solve various domains, especially, denoising, edge detection and classification problem. This paper provides a comprehensive list of global optimization algorithms that can be applied to develop innovative system and soft computing applications integrates with computational intelligence techniques. Several fitness functions and parameters implemented in the evaluation of global optimization algorithm to find global optimum for solving diverse applications are thoroughly investigated. The implications for the selection of fitness function to solve specific optimization problems and applications are also discussed.

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution this permits a rapid and broad dissemination of research results.The field of Artificial Intelligence developed important concepts for simulating human intelligence. Its sister field, Applied Intelligence, has focused on techniques for developing intelligent systems for solving real life problems in all disciplines including science, social science, art, engineering, and finance. The objective of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE) is to promote and disseminate research in Applied Intelligence. It seeks quality papers on a wide range of topics in applied intelligence that are employed in developing intelligent systems for solving real life problems in all disciplines. Every year this conference brings together scientists, engineers and practitioners, who work on designing and developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains. The book is comprised of seventeen chapters providing up-to-date and state-of-the-art research on the applications of applied Intelligence techniques.

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to getting trapped in local minima (as they are population based), and are shown to work well for many complex optimization problems. Although evolutionary algorithms have conventionally focussed on optimizing single objective functions, most practical problems in engineering are inherently multi-objective in nature. Multi-objective evolutionary optimization is a relatively new, and rapidly expanding area of research in evolutionary computation that looks at ways to address these problems. In this chapter, we provide an overview of some of the most significant issues in multi-objective optimization (Deb, 2001).

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.

Computational Intelligence is a relatively new branch of science which, to some extent, can be regarded as a successor of “traditional” Artificial Intelligence. Unlike AI, which relies on symbolic learning and heuristic approaches to problem solving, CI mainly involves systems that are inspired from nature, such as (artificial) neural networks, evolutionary computation, fuzzy systems, chaos theory, probabilistic methods, swarm intelligence, ant systems, and artificial immune systems. In a wider perspective CI includes also part of machine learning, in particular the reinforcement learning methods. Applying either one or a combination of the above-mentioned disciplines allows implementation of the elements of learning and adaptation in the proposed solutions which make such systems somehow intelligent. In the game playing domain the most popular CI disciplines are neural networks, evolutionary and neuro-evolutionary methods, and reinforcement learning. These domains are briefly introduced in the remainder of this chapter along with sample game-related applications. The focus of the presentation is on the aspects of learning and autonomous development. Relevant literature is provided for possible further reading.

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

In the last years, there has been a large growth in the research of computational techniques inspired in nature. This area, named Bioinspired Computing, has provided biologically motivated solutions for several real world problems. Among Bioinspired Computing techniques, one can mention Artificial Neural Networks (ANN), Evolutionary Algorithms (EA), Artificial Immune Systems (AIS) and Ant Colony Optimization (ACO). ACO is a meta-heuristic based on the structure and behavior of ant colonies. It has been successfully applied to several optimization problems. Several real world optimization problems may change the configuration of its search space with time. These problems are known as dynamic optimization problems. This chapter presents the main concepts of ACO and show how it can be applied to solve real optimization problems on dynamic environments. As a case study, it will be illustrated how ACO can be applied to process scheduling problems. The chapter starts describing how real ants forage for food, environmental modifications related to this activity (for example, the blockage of a path) and the effects of these modifications in the colony. After, a brief review of previous works on Ant Colony Optimization is presented. For computer simulations, the activities of an ant colony may be modeled as a graph. After modeling the problem of foraging for food (as a graph), the goal (finding the shortest path at a given moment) is defined and the mechanism to solve the problem is presented. The need to model an on-line problem as a graph, the goal of finding the shortest path and the mechanisms adopted for solving the problem (an adapted version of the AntSystem) are detailed. Before ACO is applied to the process scheduling problem, the problem is analyzed and modeled using a graph representation. Next, simulation results obtained in a set of experiments are presented, which are validated by results obtained in a real implementation. Important issues related with the use of ACO for process scheduling, like parameter adjustments, are discussed. In the conclusion of this chapter, we point out a few future directions for ACO researches. The use of computational techniques inspired in nature has become very frequent in the last years. This area, named Bioinspired Computing, provides efficient biologically motivated

Neural networks and fuzzy systems are two soft‐computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general‐purpose stochastic global optimization approach under the universally accepted neo‐Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

Nature has remedies for almost all problems. Though biological systems exhibits organised, complex and intelligent behaviour, they comprise of simple elements and governed by simple rules. Hence, mimicking such systems has been the attraction of researchers in the fields of computer science, neuroscience and biology for a long time. Generating complex behaviour from small agents working locally following simple rules is a highly cost-effective solution of the real life problem. Bio-inspired computing can be achieved through different models such as stochastic, ad hoc or discrete models; new paradigm inspired from nature like evolutionary approach and immune systems; and new platform, novel architecture and specially designed material such as artificial fuel cell. The consortium of bio-inspired computing are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing and quantum computing, etc. This article discusses consortium of bio-inspired computing along with applications and research scope. In spite of having advantages offered by partial simulation of natural intelligence, there are some limitations of the bio-inspired computing that need to be addressed. These challenges include creation of new model, techniques and platforms for bio-inspired computing. This article concludes with the challenges to be explored in the field.

Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

The Evolutionary Computing (EC) field of Computer Science has a pool of potential optimization algorithms. They are collectively termed as Evolutionary Algorithms (EAs). The Differential Evolution (DE) algorithm is added recently to this pool. It is known for its simplicity and applicability. DE differs from other EAs by its Differential Mutation logic. There exist many strategies for this mutation logic. This paper proposes a new mutation strategy which employs a self-switching base vector selection mechanism. This self-switching mechanism uses the diversity in the population as a measure to select the base vector either randomly or the best candidate in the population. It is found from the results of the experiments carried out that the proposed mechanism works better on a standard set of benchmarking functions solving unimodal optimization problems.

A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems

In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined over the benchmark functions designed for evolutionary algorithms competition in the special session of 2005 IEEE Congress on Evolutionary Computation. The used benchmark functions include the unimodal, multimodal, and hybrid benchmark functions with high dimensionality. Furthermore, the impact analysis with respect to different values of temperatures is conducted by executing the proposed algorithm twenty-five times independently by using each benchmark function with different random seeds.

The rising complexity of real-life optimization problems has constantly inspired computer researchers to develop new efficient optimization methods. Evolutionary computation and metaheuristics based on swarm intelligence are very popular nature-inspired optimization techniques. In this paper, the author has proposed a novel elephant swarm water search algorithm (ESWSA) inspired by the behaviour of social elephants, to solve different optimization problems. This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought. Initially, we perform preliminary parametric sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values in real-life problems. In addition, the algorithm is evaluated against a number of widely used benchmark functions for global optimizations, and it is observed that the proposed algorithm has better performance for most of the cases compared with other state-of-the-art metaheuristics. Moreover, ESWSA performs better during fitness test, convergence test, computational complexity test, success rate test and scalability test for most of the benchmarks. Next, ESWSA is tested against two well-known constrained optimization problems, where ESWSA is found to be very efficient in term of execution speed and best fitness. As an application of ESWSA to real-life problem, it has been tested against a benchmark problem of computational biology, i.e., inference of Gene Regulatory Network based on Recurrent Neural Network. It has been observed that the proposed ESWSA is able to reach nearest to global minima and enabled inference of all true regulations of GRN correctly with less computational time compared with the other existing metaheuristics.

Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex. In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters. With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.