A convergence algorithm for function optimization | IEEE Conference Publication | IEEE Xplore

A convergence algorithm for function optimization


Abstract:

This paper proposes an opposition-based metaheuristic optimization algorithm. The global convergence of the algorithm is guaranteed. The efficiency of the proposed method...Show More

Abstract:

This paper proposes an opposition-based metaheuristic optimization algorithm. The global convergence of the algorithm is guaranteed. The efficiency of the proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various function optimizations.
Date of Conference: 06-07 December 2016
Date Added to IEEE Xplore: 19 January 2017
ISBN Information:
Conference Location: San Francisco, CA, USA

I. Introduction

Metaheuristics have been established as one of the most practical approaches to optimization problems. They have been designed primarily to address problems that cannot be tackled through traditional optimization algorithms. Although still there is no guarantee, metaheuristic methods usually turn out to achieve better results and better performances in contrast to their classic counterparts. There are some algorithms for solving different optimization problems. However, there is no specific algorithm to achieve the best solution for all optimization problems. Some algorithms give a better solution for some particular problems than others. Hence, searching for new metaheuristic optimization algorithms is always needed.

References

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