Bio-inspired Computation Algorithms
Bio-inspired computation is the use of computers to model the living phenomena and simultaneously the study of life to improve the usage of computers. Swarm behaviors in animal groups such as bird flocks, bees, ants, fish schools, and sheep herds, as well
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Bio-inspired Computation Algorithms Pei Li and Haibin Duan
Abstract Bio-inspired computation is the use of computers to model the living phenomena and simultaneously the study of life to improve the usage of computers. Swarm behaviors in animal groups such as bird flocks, bees, ants, fish schools, and sheep herds, as well as insects like mosquitoes, ants, and bees, often exhibit incredible abilities to solve complex problems that seem far beyond their capabilities. This chapter mainly focuses on the biological inspiration, principle, and implementation procedures of four popular bio-inspired computation algorithms including ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). Special emphasis has been laid on how the biological behavior can be transferred into a technical algorithm. Moreover, description of algorithms in more general terms and the most successful variants of these algorithms are provided. Finally, a brief introduction to other bio-inspired computation algorithms such as glowworm swarm optimization (GSM), bacteria foraging optimization (BFO), bat-inspired algorithm (BA) is presented.
2.1 Introduction Bio-inspired computation, short for biologically inspired computation, is a way of developing computer systems by taking ideas from the biological world. It is a field of study that loosely knits together subfields related to the topics of connectionism, social behavior, and emergence. Briefly put, it is the use of computers to model the living phenomena and simultaneously the study of life to improve the usage of computers. Bio-inspired computation in an interdiscipline composed of many different fields, such as biology, computer science, physics, mathematics, and genetics. As we have known, biological systems have many advantages over computer systems, as they are able to solve much more complex problems beyond
H. Duan and P. Li, Bio-inspired Computation in Unmanned Aerial Vehicles, DOI 10.1007/978-3-642-41196-0__2, © Springer-Verlag Berlin Heidelberg 2014
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2 Bio-inspired Computation Algorithms
the capability of the contemporary computer with far less energy and much higher robustness. Many of the ideas taken from natural processes have been applied to machine learning, leading to new developments in artificial intelligence. Bio-inspired computation is a major subset of natural computation, which is different from traditional artificial intelligence (AI) in that it often takes a more evolutionary approach to learning. In traditional AI, as the creator, programmers give their program some degree of intelligence in the process of programming. However, bio-inspired techniques often involve the method of specifying a set of simple rules, a set of simple organisms which adhere to those rules, and a method of iteratively applying those rules. In other words, bio-inspired computation takes a more bottom-up, decentralized approach. It is a new approach to enable the intelligence, as the constructed simple system is able to
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