Swarm Intelligence And Bio Inspired Computation

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.

Swarm intelligence (SI) and bio-inspired computing in general have attracted great interest in almost every area of science, engineering, and industry over the last two decades. In this chapter, we provide an overview of some of the most widely used bio-inspired algorithms, especially those based on SI such as cuckoo search, firefly algorithm, and particle swarm optimization. We also analyze the essence of algorithms and their connections to self-organization. Furthermore, we highlight the main challenging issues associated with these metaheuristic algorithms with in-depth discussions. Finally, we provide some key, open problems that need to be addressed in the next decade.

Bio-inspired models have taken inspiration from the nature to solve challenging problems in an intelligent manner. Major aims of such bio-inspired models of computation are to propose new unconventional computing architectures and novel problem solving paradigms. Computing models such as artificial neural network (ANN), genetic algorithm (GA), and swarm intelligence (SI) are major constituent models of the bio-inspired approach. Applications of these models are ubiquitous and hence proposed to be applied for Semantic Web. The chapter discusses fundamentals of these bio-inspired constituents along with some heuristic that can be used to design and implement these constituents and briefly surveys recent applications of these models for the Semantic Web. The study shows that the objective of the Semantic Web is better met with such approach and the Web can be accessed in more human-oriented way. At the end, a generic framework for web content filtering based on neuro-fuzzy approach is presented. By considering online webpages and fuzzy user profile, the proposed system classifies the webpages into vague categories using a neural network.

Swarm intelligence refers to collective intelligence. Biologists and natural scientist have been studying the behavior of social insects due to their efficiency of solving complex problems such as finding the shortest path between their nest and food source or organizing their nests. In spite of the fact that these insects are unsophisticated individually, they make wonders as a swarm by interaction with each other and their environment. In last two decades, the behaviors of various swarms that are used in finding preys or mating are simulated into a numerical optimization technique. In this chapter, eight different swarm intelligence–based algorithms are summarized and their working steps are listed. These techniques are ant colony optimizer, particle swarm optimizer, artificial bee colony algorithm, glowworm algorithm, firefly algorithm, cuckoo search algorithm, bat algorithm, and hunting search algorithm. Two optimization problems taken from the literature are solved by all these eight algorithms and their performance are compared. It is noticed that most of the swarm intelligence–based algorithms are simple and robust techniques that determine the optimum solution of optimization problems efficiently without requiring much of a mathematical struggling.

In solving many practical mathematical programming applications, it is generally preferable to formulate several quantifiably good alternatives that provide very different approaches to the particular problem. This is because decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult—if not impossible—to quantify and capture at the time that the supporting decision models are constructed. There are invariably unmodeled design issues, not apparent at the time of model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate several alternatives that provide multiple, disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modeled objective(s) but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modeling-to-generate-alternatives (MGA). This chapter provides a synopsis of various MGA techniques and demonstrates how biologically inspired MGA algorithms are particularly efficient at creating multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. The efficacy and efficiency of these MGA methods are demonstrated using a number of case studies.

Data mining has evolved from methods of simple statistical analysis to complex pattern recognition in the past decades. During the progression, the data mining algorithms are modified or extended in order to overcome some specific problems. This chapter discusses about the prospects of improving data mining algorithms by integrating bio-inspired optimization, which has lately captivated much of researchers’ attention. In particular, high dimensionality and the unavailability of the whole data set (as in stream mining) in the training data have known to be two major challenges. We demonstrated that these two challenges, through two small examples such as K-means clustering and time-series classification, can be overcome by integrating data mining and bio-inspired algorithms.

In this chapter, we present the convergence analysis and applications of particle swarm optimization algorithm. Although it is difficult to analyze the convergence of this algorithm, we discuss its convergence based on its iterated function system and probabilistic theory. The dynamic trajectory of the particle is described based on single individual. We also attempt to theoretically prove that the swarm algorithm converges with a probability of 1 toward the global optimal. We apply the algorithms to solve the scheduling problem and peer-to-peer neighbor selection problem. This chapter is also concerned to employ the nature-inspired optimization methods in machine learning. We introduce the swarm algorithm to reoptimize hidden Markov models.

Artificial plant optimization algorithm (APOA) is a novel evolutionary strategy inspired by tree’s growing process. In this chapter, the methodologies of prototypal APOA and its updated version are illustrated. First, the primary framework is introduced by accounting for photosynthesis and phototropism phenomena. Since some important factors are ignored during mimicking branch’s growing, the optimization is sometimes misleading and time-consuming. Therefore, the standard version is developed by adding geotropism mechanism and apical dominance operator. The quality of the proposed technique is verified by two applications on artificial neural network training and toy model of protein folding. Simulation results are consistent with reported numerical data, indicating that the new optimization approach is valid and shows broad application in other fields.