Comparative Study of Meta-heuristics Optimization Algorithm using Benchmark Function

Received Jan 14, 2017 Revised Mar 20, 2017 Accepted Apr 9, 2017 Meta-heuristics optimization is becoming a popular tool for solving numerous problems in real-world application due to the ability to overcome many shortcomings in traditional optimization. Despite of the good performance, there is limitation in some algorithms that deteriorates by certain degree of problem type. Therefore it is necessary to compare the performance of these algorithms with certain problem type. This paper compares 7 meta-heuristics optimization with 11 benchmark functions that exhibits certain difficulties and can be assumed as a simulation relevant to the real-world problems. The tested benchmark function has different type of problem such as modality, separability, discontinuity and surface effects with steep-drop global optimum, bowland plateau-typed function. Some of the proposed function has the combination of these problems, which might increase the difficulty level of search towards global optimum. The performance comparison includes computation time and convergence of global optimum. Keyword: