Opposition-based multi-objective whale optimization algorithm with multi-leader guiding

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale algorithm based on multi-leader guiding is proposed this paper, which attempts to offer a proper framework apply and other swarm intelligence solving adopts several improvements enhance performance. First, search agents are classified into leadership set ordinary by grid mechanism, multiple solutions guide the population sparse spaces achieve more homogeneous exploration per iteration. Second, differential evolution employed generate offspring for solutions, respectively. In addition, novel opposition-based learning strategy developed improve distribution of initial population. performance verified contrast 10 classic or state-of-the-arts 20 bi-objective tri-objective unconstrained problems, experimental results demonstrate competitive advantages quality convergence speed. Moreover, it tested load hot rolling, result proves its good real-world applications. Thus, all aforementioned experiments indicated that comparatively effective efficient.