Lifecycle-Based Swarm Optimization Method for Numerical Optimization

Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 2014, Article ID 892914, 11 pages http://dx.doi.org/10.1155/2014/892914 Research Article Lifecycle-Based Swarm Optimization Method for Numerical Optimization Hai Shen,1,2 Yunlong Zhu,2 and Xiaodan Liang3 1 College of Physics Science and Technology, Shenyang Normal University, Shenyang 110023, China Laboratory of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang, Shenyang 110016, China 3 School of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China 2 Correspondence should be addressed to Yunlong Zhu; Received 19 October 2014; Revised 18 November 2014; Accepted 23 November 2014; Published 11 December 2014 Academic Editor: Muhammad Naveed Iqbal Copyright © 2014 Hai Shen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques. 1. Introduction In nature, biology species are divers and an organism is any living thing (such as animal, plant, or microorganism) [1]. All their behaviors can show what kind of biological features they have. Some features are universality, such as foraging, reproduction, mutation, and metabolism. And for some organisms, their features are uniqueness and intelligence [2]. The ant possesses division and cooperation behaviors. Bees have special skills in the process of gathering honey. Birds have unique flight principle. The bacterial flagellums play a role of chemotaxis in their moving. Biologic features enable organisms to adapt to the complex living environment in the best way and long-term survival in nature. Real-world optimization problems are similar to biologic survival environment; they all have complex features. Therefore, with the purpose of solving reality complex problem, researchers begin to mimic the biologic phenomena via defining a set of rules and realize those rules on computer [3]. Those rules are called bioinspired optimization technique. Currently, the bioinspired optimization techniques possessing abundant research results, and we divide all existing algorithms into three major categories: evolutionary computation, swarm intelligence, and others. Widely concerned algorithms are as follows: (1) evolutionary computation: (i) genetic algorithm; (ii) evolutionary programming; (iii) evolutionary strategy; (iv) genetic programming; (v) differential evolution; (vi) neuroevolution; 2 Discrete Dynamics in Nature and Society (2) swarm intelligence (SI): (i) particle swarm optimization; (ii) ant colony optimization; (iii) bacterial foraging optimization algorithm; (iv) artificial bee colony; (v) shuffled frog leaping algorithm; (vi) glowworm swarm optimization; (vii) cuckoo search; (viii) firefly algorithm; (ix) harmony search; (x) bat algorithm; (xi) wolf search; (3) other algorithms: (i) artificial immune algorithm; (ii) artificial neural networks; (iii) cellular automata; (iv) cultural algorithm; (v) membrane computers; (vi) brain storm optimization; (vii) ecoinspired evolutionary algorithm; (viii) invasive weed optimization; (ix) dolphin echolocation. Moreover, these bioinspired optimization algorithms have been widely applied to network optimization [4–7], data mining [8–10], production scheduling [11–14], power system [15, 16], pattern recognition [17, 18], robotics applications [19– 21] and so on. All living organisms have lifecycle, either the commonest ants, butterflies, goldfish around us or the uncommon Antarctic penguins, arctic bear or either the ferocious beast or the meek of poultry. Although different organisms have different lifecycle lengths, they all undergo the process from birth to death. When an original life ends, a new life will generate. The biology evolution of nature follows the “cycle relay” pattern, which is a “life and death alternation” cycle process. This process repeated continuously made the endless life on earth, and biologic evolution become more and more perfect. Inspired by the idea of lifecycle, in 2002, Krink and Løvbjerg introduced a hybrid approach called the lifecycle model that simultaneously applies genetic algorithms (GAs), particle swarm optimization (PSO), and stochastic hill climbing to create a generally well-performing search heuristics [22]. In this model, authors consider candidate solutions and their fitness as individuals, which, based on their recent search progress, can decide to become either a GA individual, a particle of a PSO, or a single stochastic hill climber. In 2008, Niu et al. proposed a lifecycle model (LCM) to simulate bacterial evolution from a finite population of Escherichia coli (E. coli) bacteria [23]. In this simulation study, bacterial behaviors (chemotaxis, reproduction, extinction, and migration) during their whole life cycle are viewed as evolutionary operators used to find the best nutrient concentration which is labeled as a potential global solution of the optimization problem. In 2011, borrowing the biologic lifecycle theory, the Lifecycle-based swarm optimization (LSO) algorithm was proposed for the first time [24]. Then, 7 unimodal unconstrained optimization test functions and constrained optimization test functions as well as engineering problems that include vehicle routing problem (VRP) and vehicle routing problem with Time Windows (VRPTW) were adopted to test LSO algorithm performance [24–26]. The above experiments demonstrate that LSO is a competitive and effective approach. In order to evaluate the LSO performance accurately, this paper uses 23 (...truncated)