A new algorithm inspired in the behavior of the social-spider for constrained optimization |
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Affiliation: | 1. Fraunhofer INT, Appelsgarten 2, D-53879 Euskirchen, Germany;2. Ghent University, Faculty of Economics and Business Administration, Tweekerkenstraat 2, B-9000 Gent, Belgium;1. School of IOT Engineering, Jiangnan University, Wuxi 214122, China;2. Department of Electronics and Information Engineering, Chonbuk National University, Jeonju, Jeonbuk 561756, Republic of Korea;1. Badji Mokhtar University, LRS, Annaba, Algeria;2. Badji Mokhtar University, LRI, Annaba, Algeria;3. Université de Lorraine, LORIA, Nancy, France;4. CNRS UMR 7503, Nancy, France;5. Inria Nancy Grand Est, France;1. College of Biomedical Engineering and Instrument Science, Zhejiang University, 310008 Zhou Yiqing Building 510, Zheda road 38#, Hangzhou, Zhejiang, China;2. Department of Information and Communication Engineering, University of Murcia, Spain;1. Institute of Elec and Information Engineering in Hunan University, Changsha 410082, China;2. INESC Porto and Faculty of Engineering, University of Porto, Porto 4099-002, Portugal;3. College of Computer Science, Beijing University of Information Science and Technology, Beijing, China |
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Abstract: | During the past decade, solving constrained optimization problems with swarm algorithms has received considerable attention among researchers and practitioners. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. The SSO-C algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. For constraint handling, the proposed algorithm incorporates the combination of two different paradigms in order to direct the search towards feasible regions of the search space. In particular, it has been added: (1) a penalty function which introduces a tendency term into the original objective function to penalize constraint violations in order to solve a constrained problem as an unconstrained one; (2) a feasibility criterion to bias the generation of new individuals toward feasible regions increasing also their probability of getting better solutions. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. Simulation and comparisons based on several well-studied benchmarks functions and real-world engineering problems demonstrate the effectiveness, efficiency and stability of the proposed method. |
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Keywords: | Swarm algorithms Constrained optimization Bio-inspired algorithms |
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