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jMetal: A Java framework for multi-objective optimization
Juan J. Durillo,Antonio J. Nebro +1 more
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This paper describes jMetal, an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving multi-objective optimization problems, and includes two case studies to illustrate the use of jMetal in both solving a problem with a metaheuristic and designing and performing an experimental study.About:
This article is published in Advances in Engineering Software.The article was published on 2011-10-01. It has received 1025 citations till now. The article focuses on the topics: Metaheuristic & Multi-objective optimization.read more
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Journal ArticleDOI
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
TL;DR: In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space, and reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization.
Journal ArticleDOI
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]
TL;DR: PlatEMO as discussed by the authors is a MATLAB platform for evolutionary multi-objective optimization, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multobjective test problems, along with several widely used performance indicators.
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PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
TL;DR: The main features of PlatEMO are introduced and how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators are illustrated.
Journal ArticleDOI
Pymoo: Multi-Objective Optimization in Python
Julian Blank,Kalyanmoy Deb +1 more
TL;DR: This work develops pymoo, a multi-objective optimization framework in Python that addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making.
Journal ArticleDOI
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization
Yuan Yuan,Hua Xu,Bo Wang,Xin Yao +3 more
TL;DR: In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization that aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition.
References
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Proceedings Article
Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms
TL;DR: This study illustrates how a technique such as the multiobjective genetic algorithm can be applied and exemplifies how design requirements can be refined as the algorithm runs, and demonstrates the need for preference articulation in cases where many and highly competing objectives lead to a nondominated set too large for a finite population to sample effectively.
Proceedings ArticleDOI
SMPSO: A new PSO-based metaheuristic for multi-objective optimization
Antonio J. Nebro,Juan J. Durillo,José García-Nieto,Carlos A. Coello Coello,Francisco Luna,Enrique Alba +5 more
TL;DR: A new multi-objective particle swarm optimization algorithm characterized by the use of a strategy to limit the velocity of the particles, called Speed-constrained Multi-Objective PSO (SMPSO), which allows to produce new effective particle positions in those cases in which the velocity becomes too high.
Journal Article
PISA: A platform and programming language independent interface for search algorithms
TL;DR: In this article, the problem representation together with the variation operators is seen as an integral part of the optimization problem and can hence be easily separated from the selection operators, which makes it possible to specify and implement representation-independent selection modules, which form the essence of modern multiobjective optimization algorithms.
Journal ArticleDOI
Testing Heuristics: We Have It All Wrong
TL;DR: This article argues that a more scientific approach of controlled experimentation, similar to that used in other empirical sciences, avoids or alleviates problems of competitive testing in algorithmic experimentation.
Journal ArticleDOI
A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm
A. Osyczka,S. Kundu +1 more
TL;DR: A new method of transformation of the multiple criteria problem into a single-criterion problem is presented and preliminary computer generated results show that this approach produces better, and far more Pareto solutions, than plain stochastic optimization methods.
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