A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization
ملخص البحث
Abstract: Multi-Objective Problems (MOPs) are common real-life problems that can be found in
different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular
method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective
way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are
population-based methods that generate multiple solutions to the problem, providing SI methods
suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs,
such as swarm leader selection and obtaining evenly distributed solutions over solution space.
Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining
WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated
solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem.
MOWOATS employs crossover in both intensification and diversification phases to improve diversity
of the population. MOWOATS proposes a new diversification step to eliminate the need for local
search methods. MOWOATS has been tested over different benchmark multi-objective test functions,
such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions
near Pareto front and evenly distributed over solution space.
الكلمات المفتاحيه
Keywords: Multi-Objective Optimization; Multi-Objective Problems; Pareto Optimization; Swarm Intelligence; Tabu Search; Whale Optimization Algorithm