Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

In this work, a new multi-objective optimization algorithm called learner performance-based behavior is proposed. The proposed based on the process of moving graduated students from high school to college. technique produces set non-dominated solutions. To test ability and efficacy algorithm, it applied group benchmarks five real-world engineering problems. Several widely used metrics are employed in quantitative statistical comparisons. compared with three algorithms: Multi-Objective Water Cycle Algorithm (MOWCA), Non-dominated Sorting Genetic (NSGA-II), Dragonfly (MODA). produced results for problems show that general accuracy diversity better MOWCA MODA. However, NSGA-II outperformed work some cases showed diversity. Nevertheless, problems, such as coil compression spring design problem, quality solutions by all participated algorithms. Moreover, regard processing time, provided