Genghis Khan Shark Optimizer Based Approach for Multi-Objective Engineering Problems

This study introduces a new multi-objective approach called the Non-Dominated Genghis Khan Shark Opti-mizer (NSGKSO) for tackling mechanical engineering challenges. The GKSO's inspiration is derived from the natural behaviors of GKS, structuring its process of optimization by mimicking four spe...

Full description

Saved in:
Bibliographic Details
Published in:International Conference on Optimization and Applications (Online) pp. 1 - 6
Main Authors: Daqaq, Fatima, Ouhimmou, Siham
Format: Conference Proceeding
Language:English
Published: IEEE 17.10.2024
Subjects:
Benchmark testing
Complexity theory
Estimation
Genghis Khan shark optimizer
Measurement
Mechanical engineering
Mechanical engineering design problems
Multi-objective optimization
Non-dominated solutions
Optimization methods
Robustness
Sharks
Sorting
ISSN:2768-6388
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study introduces a new multi-objective approach called the Non-Dominated Genghis Khan Shark Opti-mizer (NSGKSO) for tackling mechanical engineering challenges. The GKSO's inspiration is derived from the natural behaviors of GKS, structuring its process of optimization by mimicking four specific behaviors of GKSs: exploration (hunting), exploitation (movement), transitioning from exploration to exploitation (foraging), and a self-defense mechanism. Following the elitist non-dominated sorting mechanism in the NSGKSO algorithm, a non-dominated ranking approach is combined with a crowding distance calculation. This integration aims to archive the Pareto front and enhance the coverage of optimal solutions. For evaluating the performance of NSGKSO, a series of benchmark tests are conducted, encompassing standard constrained and unconstrained optimization functions, along with four mechanical engineering design problems. The study includes a comparative analysis between the proposed NSGKSO technique and other established evolutionary optimization methods. Statistical findings showcase the robustness of the NSGKSO approach, assessed through metrics such as inverted generational distance (IGD), generalized distance (GD), and spacing (SP). Moreover, qualitative experimental outcomes affirm that NSGKSO offers highly precise approximations of the real Pareto front.
ISSN:2768-6388
DOI:10.1109/ICOA62581.2024.10754310