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

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 46; no. 1; pp. 79 - 95
• Main Authors: Mirjalili, Seyedali, Jangir, Pradeep, Saremi, Shahrzad
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2017; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s10489-016-0825-8

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.