Abstract
An advanced chemical reaction optimization algorithm based on balanced local search and global search is proposed, which combines the advantages of adaptive chemical reaction optimization (ACRO) and particle swarm optimization (PSO), to solve continuous optimization problems. This new optimization is mainly based on the framework of ACRO, with PSO’s global search operator applied as part of ACRO’s neighborhood search operator. Moreover, a “finish” operator is added to the ACRO’s structure and the search operator is evolved by an adaptive scheme. The simulation results tested on a set of twenty-three benchmark functions, and a comparison was made with the results of a newly proposed hybrid algorithm based on chemical reaction optimization (CRO) and particle swarm optimization (denoted as HP-CRO). The finial comparison results show the superior performance improvement over HP-CRO in most experiments.
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