Opposition Based Local Escaping Marine Predators Algorithm for Continuous Optimization

The marine predators algorithm (MPA) is a recently developed metaheuristic algorithm that is inspired by the foraging behavior of marine predators. It has been widely used to solve real-life optimization problems. However, it frequently gets trapped in a local optima since it is unable to have a diversified population in the early stages of optimization. To overcome the premature convergence problem of MPA, this paper introduces an improved version of the marine predators algorithm named as opposition-based local escaping marine predators algorithm (OLMPA). There are two ways in which the improvement is carried out. The first improvement uses opposition based learning (OBL) assures the diversity of solutions in the search space. The second improvement uses the local escaping operation, which creates new solutions that replace the worst solutions to estimate the best solution. These enhancements are designed to address the imbalance between exploration and exploitation. The proposed OLMPA is tested on 23 benchmark functions. The numerical and statistical experimental results show that the proposed algorithm overperformed classical MPA.