Modified Seeker Optimization Algorithm for Unconstrained Optimization Problems

摘要

Seeker optimization algorithm (SOA) is a novel search algorithm based on simulating the act of human searching, which has been shown to be a promising candidate among search algorithms for unconstrained function optimization. In this article we propose a modified seeker optimization algorithm. In order to enhance the performance of SOA, our proposed approach uses two search equations for producing new population and employs modified inter-subpopulation learning phase of algorithm. This modified algorithm has been implemented and tested on fourteen multimodal benchmark functions and proved to be better on majority of tested problems.