Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems

This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a ro...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 46; H. 1; S. 79 - 95
Hauptverfasser: Mirjalili, Seyedali, Jangir, Pradeep, Saremi, Shahrzad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2017
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Brake disks
Cantilever beams
Computer Science
Computers
Design
Design engineering
Engineering design
Genetic algorithms
Literature reviews
Machines
Manufacturing
Mechanical Engineering
Multiple objective analysis
Objectives
Optimization algorithms
Optimization techniques
Pareto optimum
Processes
Repositories
Wheels
Multi-criterion optimization
Evolutionary algorithm
Engineering optimization
Multi-objective optimization
Meta-heuristic
Ant lion optimizer
Algorithm
Optimization
Heuristic
ISSN:0924-669X, 1573-7497
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Zusammenfassung:This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a roulette wheel mechanism based on the coverage of solutions as antlions to guide ants towards promising regions of multi-objective search spaces. To prove the effectiveness of the algorithm proposed, a set of standard unconstrained and constrained test functions is employed. Also, the algorithm is applied to a variety of multi-objective engineering design problems: cantilever beam design, brushless dc wheel motor design, disk brake design, 4-bar truss design, safety isolating transformer design, speed reduced design, and welded beam deign. The results are verified by comparing MOALO against NSGA-II and MOPSO. The results of the proposed algorithm on the test functions show that this algorithm benefits from high convergence and coverage. The results of the algorithm on the engineering design problems demonstrate its applicability is solving challenging real-world problems as well.
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-016-0825-8