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A novel optimization method based on opinion formation in complex networks

Hamed Moghadam Rafati, H ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1109/ISCAS.2016.7527382
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2016
  4. Abstract:
  5. In this paper we introduce a novel population-based binary optimization technique, which works based on consensus of interacting multi-agent systems. The agents, each associated with an opinion vector, are connected through a network. They can influence each other, and thus their opinions can be updated. The agents work collectively with their neighbors to solve an optimization task. Here we consider a specific opinion update rule and various topologies for the connection network. Our experiments on a number of benchmark non-convex cost functions show that ring topology results in the best performance as compared to others. We also compare the performance of the proposed method with a number of well-known optimizers (genetic algorithms, binary particle swarm optimizer, and binary differential evolution) and show its outperformance over them. The proposed optimizer also shows rather fast convergence to the optimal solution. © 2016 IEEE
  6. Keywords:
  7. Non-convex optimization ; population-based binary optimization methods ; Benchmarking ; Bins ; Circuit theory ; Convex optimization ; Cost functions ; Evolutionary algorithms ; Genetic algorithms ; Multi agent systems ; Optimization ; Particle swarm optimization (PSO) ; Reconfigurable hardware ; Topology ; Binary optimization ; Binary particle swarm ; Differential Evolution ; Nonconvex cost functions ; Nonconvex optimization ; Opinion formation ; Optimization method ; Social dynamics ; Complex networks
  8. Source: 2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016, 22 May 2016 through 25 May 2016 ; Volume 2016-July , 2016 , Pages 882-885 ; 02714310 (ISSN); 9781479953400 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7527382/?arnumber=7527382