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Swarm-based intelligent optimization approach for layout problem

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Abstract

Layout problem is a kind of NP-Complete problem. It is concerned more and more in recent years and arises in a variety of application fields such as the layout design of spacecraft modules, plant equipment, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are considered powerful tools for solving this kind of problems. While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In proposed approach, chaos initialization and multi-subpopulation evolution strategy based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure is adaptive with evolution process. That is to say, it can avoid early premature as well as benefit speeding up convergence of later period effectively. And more importantly, proposed PSO update operators based on different versions PSO are introduced into presented algorithm. It can take full advantage of the outstanding convergence characteristic of particle swarm optimization (PSO) and improve the global performance of the proposed algorithm. An example originated from layout of printed circuit boards (PCB) and plant equipment shows the feasibility and effectiveness of presented algorithm.

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Acknowledgments

Our research work is financially supported by the National Natural Science Foundation of China (No. 61374114 and No. 51579024), the Fundamental Research Funds for the Central Universities of China (No. 3132014321, No. DC120101014, No. DC110320), the Applied Basic Research Program of Ministry of Transport of China (No. 2011-329-225-390, No. 2012-329-225-070), the China Scholarship council (No. 201306575010), the Higher Education Research Fund of Education Department of Liaoning Province of China (No. LT2010013), and the Doctor Startup Foundation of Liaoning Province (No. 20131006).

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Authors and Affiliations

  1. School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Fengqiang Zhao, Guangqiang Li, Jialu Du, Chen Guo & Yiran Zhou

  2. College of Mechanical and Electronic Engineering, Dalian Nationalities University, Dalian, 116600, China

    Fengqiang Zhao & Rubo Zhang

  3. Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, 01854, United States

    Guangqiang Li

  4. Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China

    Zhihan Lv

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  1. Fengqiang Zhao
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  2. Guangqiang Li
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  4. Jialu Du
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  5. Chen Guo
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  6. Yiran Zhou
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Correspondence to Fengqiang Zhao, Guangqiang Li or Zhihan Lv.

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Zhao, F., Li, G., Zhang, R. et al. Swarm-based intelligent optimization approach for layout problem. Multimed Tools Appl 76, 19445–19461 (2017). https://doi.org/10.1007/s11042-015-3174-4

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  • DOI: https://doi.org/10.1007/s11042-015-3174-4

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