A Generalized Framework for Multi-objective-based Constraint Handling Technique

This paper introduces a novel approach to handling constraints in multi-objective optimization problems by treating constraints as objectives. Our study consists of three main contributions. First, we present a generalized framework for multi-objective-based constraint handling techniques, which int...

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Vydáno v:International journal of control, automation, and systems Ročník 22; číslo 10; s. 3240 - 3252
Hlavní autoři: Dam, Tien Minh, Truong, Long Viet
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.10.2024
Springer Nature B.V
제어·로봇·시스템학회
Témata:
Constraints
Control
Engineering
Genetic algorithms
Handling
Mechatronics
Multiple objective analysis
Objectives
Optimization
Pareto optimum
Regular Papers
Robotics
Violations
제어계측공학
constraint handling techniques
metaheuristic optimization algorithm
Constrained multi-objective evolutionary algorithm
constrained multi-objective optimization problem
constraint as objectives
generalized framework for constraint handling
ISSN:1598-6446, 2005-4092
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Shrnutí:This paper introduces a novel approach to handling constraints in multi-objective optimization problems by treating constraints as objectives. Our study consists of three main contributions. First, we present a generalized framework for multi-objective-based constraint handling techniques, which integrates multi-objective-based methods with the constraint dominance principle. This framework systematically incorporates various aspects of constraint violations into the objective function, enhancing optimization strategies. Second, we propose the constraint violation ratio, a metric that uses a constraint weight vector to quantify the severity of violations. Third, we develop the constraint diversity factor, an adaptive version of the constraint weight vector, which adapts as the frequency of constraint violations changes. Extensive experimental evaluations validate the effectiveness of our methods, demonstrating their applicability across a wide range of constrained optimization scenarios.
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http://link.springer.com/article/10.1007/s12555-024-0445-8
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-024-0445-8