MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems

This paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and crowding distance mechanism. The proposed algorithm is based on the recently proposed Marine-Predator Algorithm, and it was inspired by biolog...

Full description

Saved in:
Bibliographic Details
Published in:Evolutionary intelligence Vol. 16; no. 1; pp. 169 - 195
Main Authors: Jangir, Pradeep, Buch, Hitarth, Mirjalili, Seyedali, Manoharan, Premkumar
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Springer Nature B.V
Subjects:
Algorithms
Applications of Mathematics
Archives & records
Artificial Intelligence
Benchmarks
Bioinformatics
Control
Crowding
Design engineering
Engineering
Heuristic
Linear programming
Mathematical and Computational Engineering
Mechatronics
Multiple objective analysis
Objectives
Optimization
Pareto optimization
Pareto optimum
Predators
Research Paper
Robotics
Statistical Physics and Dynamical Systems
Success
Crowding distance
Multi-objective optimization
Real-world engineering problems
Metaheuristics
Non-dominated sorting mechanism
ISSN:1864-5909, 1864-5917
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and crowding distance mechanism. The proposed algorithm is based on the recently proposed Marine-Predator Algorithm, and it was inspired by biological interaction between predator and prey. The proposed MOMPA can address multiple and conflicting objectives when solving optimization problems. The MOMPA is formulated using elitist non-dominated sorting and crowding distance mechanisms. The proposed method is tested on various multi-objective case studies, including 32 unconstrained, constraint, and engineering design problems with different linear, nonlinear, continuous, and discrete characteristics-based Pareto front problems. The results of the proposed MOMPA are compared with several well-regarded Multi-Objective Water-Cycle Algorithm, Multi-Objective Symbiotic-Organism Search, Multi-Objective Moth-Flame Optimizer algorithms qualitatively and quantitatively using several performance indicators. The experimental results demonstrate the merits of the proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-021-00649-z