A nature-inspired meta-heuristic knowledge-based algorithm for solving multiobjective optimization problems

Abstract The effectiveness of meta-heuristics has recently been well demonstrated. However, there will be a need for reliable algorithms that can handle problems in the real world. The multiobjective nature-inspired meta-heuristic knowledge-based (NMHK) algorithm is an advanced version of the gainin...
Ausführliche Beschreibung

Abstract The effectiveness of meta-heuristics has recently been well demonstrated. However, there will be a need for reliable algorithms that can handle problems in the real world. The multiobjective nature-inspired meta-heuristic knowledge-based (NMHK) algorithm is an advanced version of the gaining-sharing knowledge optimization (GSK) algorithm, which is available in the literature. NMHK is designed specifically for tackling multiobjective optimization problems (MOPs). Knowledge-sharing algorithms are essential for easing the transfer of knowledge and expertise between people and groups. It is possible to significantly improve organizational learning, problem-solving, and decision-making by utilizing the collective knowledge and abilities of individuals. The NMHK algorithm, which is described in this paper, intends to improve the process of obtaining and spreading of knowledge. Moreover, the experimental results highlight the proposed NMHK algorithm’s overall speedy performance, particularly when applied to realistic optimization problems. Ausführliche Beschreibung