A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

Abstract

Metaheuristics in recent years has proven its effectiveness; however, robust algorithms that can solve real-world problems are always needed. In this paper, we suggest the first extended version of the recently introduced gaining–sharing knowledge optimization (GSK) algorithm, named multiobjective gaining–sharing knowledge optimization (MOGSK), to deal with multiobjective optimization problems (MOPs). MOGSK employs an external archive population to store the nondominated solutions generated thus far, with the aim of guiding the solutions during the exploration process. Furthermore, fast nondominated sorting with crowding distance was incorporated to sustain the diversity of the solutions and ensure the convergence towards the Pareto optimal set, while the ϵ-dominance relation was used to update the archive population solutions. ϵ-dominance helps provide a good boost to diversity, coverage, and convergence overall. The validation of the proposed MOGSK was conducted using five biobjective (ZDT) and seven three-objective test functions (DTLZ) problems, along with the recently introduced CEC 2021, with fifty-five test problems in total, including power electronics, process design and synthesis, mechanical design, chemical engineering, and power system optimization. The proposed MOGSK was compared with seven existing optimization algorithms, including MOEAD, eMOEA, MOPSO, NSGAII, SPEA2, KnEA, and GrEA. The experimental findings show the good behavior of our proposed MOGSK against the comparative algorithms in particular real-world optimization problems.

A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

“…Moreover, owing to their flexible architecture and versatile capability, MOEAs have also been applied into many real-world complex optimization problems [28][29][30][31][32] including discrete optimization problems such as network community detection problems [33], neural network search problems [34], task offload problems [35,36], and feature selection problems [37][38][39]. In particular, feature selection has been widely used as a data preprocessing and dimensionality reduction technique for tackling large-scale classification datasets by selecting only a subset of useful features [40].…”

A Dynamic Tasking-Based Evolutionary Algorithm for Bi-Objective Feature Selection

“…There are numerous other kinds of excellent MOEAs [57][58][59][60] that have been proposed around the world, many of which are used for real-world applications, such as intrusion detection in networks [61], efficient sensing in wireless sensor networks [62], control of building systems [63], menu planning in schools [64] and control of hybrid electric vehicle charging systems [65].…”