Greedy opposition-based learning for chimp optimization algorithm

The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Although ChOA has shown promising results on optimization functions, it suffers from a slow convergence rate and low exploration capability. Therefore, in this paper, a modified ChOA is proposed to improve the exploration and exploitation capabilities of the ChOA. This improvement is performed using greedy search and opposition-based learning (OBL), respectively. In order to investigate the efficiency of the OBLChOA, the OBLChOA's performance is evaluated by twenty-three standard benchmark functions, ten suit tests of IEEE CEC06-2019, randomly generated landscape, and twelve real-world Constrained Optimization Problems (IEEE COPs-2020) from a variety of engineering fields, including industrial chemical producer, power system, process design and synthesis, mechanical design, power-electronic, and livestock feed ration. The results are compared to benchmark optimizers, including CMA-ES and SHADE as high-performance optimizers and winners of IEEE CEC competition; standard ChOA; OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. OBLChOA and CMA-ES rank first and second among twenty-seven numerical test functions, respectively, with forty and eleven best results. In the 100-digit challenge, jDE100 achieves the highest score of 100, followed by DISHchain1e + 12, and OBLChOA achieves the fourth-highest score of 93. In total, eighteen state-of-the-art algorithms achieved the highest score in seven out of ten issues. Finally, OBLChOA and CMA-ES achieve the best performance in five and four real-world engineering challenges, respectively.