Improved Manta Ray Foraging Optimization Using Opposition-Based Learning for Optimization Problems

Abstract

Manta ray foraging optimization (MRFO) algorithm is a bio-inspired meta-heuristic algorithm. It has been proposed as an alternative optimization approach for real-world engineering problems. However, MRFO is not good at fine-tuning of solutions around optima and suffers from slow convergence speed because of its stochastic nature. It needs to be improved due to latter issues. Therefore, in this study, opposition-based learning (OBL) technique was used together with MRFO in order to obtain an effective structure for optimization problems. The proposed structure has been named as opposition-based Manta ray foraging optimization (OBL-MRFO). In the proposed algorithm, the advantage of OBL in terms of considering the opposite solutions was used to have an algorithm with better performance. The proposed algorithm has been tested on four different benchmark functions such as Sphere, Rosenbrock, Schwefel and Ackley. Statistical analyses were performed through comparing the performance of OBL-MRFO with the other algorithms such as salp swarm algorithm, atom search optimization and original MRFO. The results showed that the proposed algorithm is more effective and has better performance than other algorithms.