Contour Gradient Optimization

Authors:
Zhou Wu;Tommy W. S. Chow;Shi Cheng;Yuhui Shi
Affiliations:
Department of Electrical and Electronic Engineering, City University of Hong Kong, Hong Kong;Department of Electrical and Electronic Engineering, City University of Hong Kong, Hong Kong;Division of Computer Science, The University of Nottingham Ningbo, Ningbo, China;Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China
Venue:
International Journal of Swarm Intelligence Research
Year:
2013

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Abstract

Inspired by the local cooperation behavior in the real world, a new evolutionary algorithm Contour Gradient Optimization algorithm CGO is proposed for solving optimization problems. CGO is a new type of global search algorithm that emulates the cooperation among neighbors. Each individual in CGO evolves in its neighborhood environment to find a better region. Each individual moves with a velocity measured by the field of its nearest individuals. The field includes the attractive forces from its better neighbor in the higher contour level and the repulsive force from its worse neighbor in the lower contour level. Intensive simulations were performed and the results show that CGO is able to solve the tested multimodal optimization problems globally. In this paper, CGO is thoroughly compared with six different widely used optimization algorithms under sixteen different benchmark functions. The comparative analysis shows that CGO is comparatively better than these algorithms in the respect of accuracy and effectiveness.