EWSO: Boosting White Shark Optimizer for solving engineering design and combinatorial problems - Houssein, Essam H.; Saeed, Mahmoud Khalaf; Al-Sayed, Mustafa M. (Mathematics and computers in simulation, 2024)
Population-based meta-heuristic algorithms are crucial for solving optimization issues. One of these recent algorithms that is now believed to be promising metaheuristic algorithm is the White Shark Optimizer (WSO). Although it has produced a number of encouraging results, it has some certain downsides like other metaheuristic algorithms (MAs). Dropping into the local minimum optima and local solution zones, the uneven distribution of exploration and exploitation abilities, and the slow rate of convergence are some of these downsides. To fight those, two efficient mechanisms, i.e., Enhanced Solution Quality (ESQ) and Orthogonal Learning (OL), have been applied to develop an enhanced version of WSO called EWSO. The effectiveness of EWSO has been comprehensively evaluated using the IEEE CEC’2022 test suite. For further verification and achieving the principle of generality, the proposed algorithm has been used to provide good solutions for three engineering design issues (i.e., Gear train, Vertical deflection of an I beam, and the piston lever), for further applicability it has also been employed to solve two combinatorial optimization problems (i.e., bin packing problem (BPP) and quadratic assignment problems (QAP)). This effectiveness has been evaluated compared to the most recent and common metaheuristics, i.e., Kepler Optimization Algorithm (KOA), Seagull Optimization Algorithm (SOA), Spider Wasp Optimizer (SWO), and some well-known metaheuristic algorithms such as; Sine cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Trees Social Relations Optimization (TSR), in addition to the original SWO. The experimental results and statistical measures confirm the effectiveness and reliability of the proposed algorithm (EWSO) in tackling real-world issues. It is able to overcome the previous drawbacks by providing the global optimum and preventing premature convergence through an increase in population diversity. •Enhanced EWSO algorithm is presented based on ESQ and OL strategies.•The EWSO method is proposed for solving various optimization problems.•The algorithm performance is verified on IEEE CEC’2022 test suite.•The method performance is verified on three different engineering design problems and two combinatorial optimization problems.•Efficiency of the proposed method is compared with many metaheuristics.