Opposition-Based Animal Migration Optimization

Abstract EN

AMO is a simple and efficient optimization algorithm which is inspired by animal migration behavior.

However, as most optimization algorithms, it suffers from premature convergence and often falls into local optima.

This paper presents an opposition-based AMO algorithm.

It employs opposition-based learning for population initialization and evolution to enlarge the search space, accelerate convergence rate, and improve search ability.

A set of well-known benchmark functions is employed for experimental verification, and the results show clearly that opposition-based learning can improve the performance of AMO.