An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization |
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Authors: | Junling Zhang Changyong Liang Jian Wu |
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Institution: | a Institute of Computer Network Systems, Hefei University of Technology, Tunxi Road No. 193, Hefei 230009, China b Department of Computer Science and Technology, Tongling University, Tongling 244000, China c School of Business Administration, Zhejiang Normal University, Jinhua 321004, China |
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Abstract: | Multiagent systems have been studied and widely used in the field of artificial intelligence and computer science to catalyze computation intelligence. In this paper, a multiagent evolutionary algorithm called RAER based on the ERA multiagent modeling pattern is proposed, where ERA has the same architecture as Swarm including three parts of Environment, Reactive rules and Agents. RAER integrates a novel roulette inversion operator (RIO) proposed in this paper and theoretically proved to conquer the irrationality of the inversion operator (IO) designed by John Holland when used for real code stochastic optimization algorithms. Experiments for numerical optimization of 4 benchmark functions show that the RIO operator bears better functioning than IO operator. And experiments for numerical optimization of 12 benchmark functions are used to examine the performance and scalability of RAER along the problem dimensions ranging 20-10 000, results indicate that RAER outperforms other comparative algorithms significantly. Also, two engineering optimization problems of a stable linear system approximation and a welded beam design are used to examine the applicability of RAER. Results show that RAER has better search ability and faster convergence speed. Especially for the approximation problem, REAR can find the proper optima belonging to different fixed search areas, which is significantly better than other algorithms and shows that RAER can search the problem domains more thoroughly than other algorithms. Hence, RAER is efficient and practical. |
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Keywords: | Multiagent system Inversion operator Evolutionary algorithm Numerical optimization Linear system Design optimization |
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