EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization
- PDF / 1,920,241 Bytes
- 26 Pages / 595.276 x 790.866 pts Page_size
- 56 Downloads / 305 Views
ORIGINAL ARTICLE
EMoSOA: a new evolutionary multi‑objective seagull optimization algorithm for global optimization Gaurav Dhiman1 · Krishna Kant Singh2 · Adam Slowik3 · Victor Chang4 · Ali Riza Yildiz5 · Amandeep Kaur6 · Meenakshi Garg7 Received: 27 September 2019 / Accepted: 20 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominated Pareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four realworld engineering design problems are validated using proposed EMoSOA algorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from the Pareto which shows high convergence. Keywords Seagull Optimization Algorithm · Multi-objective Optimization · Evolutionary · Pareto · Engineering Design Problems · Convergence · Diversity
1 Introduction
The source codes are available at: http://dhimangaurav.com/. * Gaurav Dhiman [email protected] 1
Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab 147001, India
2
Department of Electronics and Communication Engineering, KIET Group of Institution, Delhi‑NCR, Ghaziabad, India
3
Department of Electronics and Computer Science, Koszalin University of Technology, Sniadeckich 2, 75‑453 Koszalin, Poland
4
School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK
5
Department of Automotive Engineering, College of Engineering, Uludag University, Grkle, Bursa 16059, Turkey
6
Department of Computer Science and Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India
7
Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab 147001, India
In recent decades, metaheuristic techniques of optimization have provided tremendous attention from researchers to address actual search and problems with optimization. The techniques are mathematically tractable, relatively affordable, and faster than exhaustive searches. Such approaches aim to achieve near-optimal solutions [1–11]. In general, metaheuristic optimization strategies can be divided into single-objective and multi-objective categories. The goal of single-objective techniques is to provide the only global best way of optimizing the single-objective function [12]. Nonetheless, multiple objectives must be addressed concurrently on most of the problems of real-life optimization. Because these
Data Loading...
