Journal ArticleDOI
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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An improved decomposition-based multiobjective evolutionary algorithm with a better balance of convergence and diversity
TL;DR: This work considered using the value of related acute angle between a solution and a direction vector as an other consider index to enhance the famous decomposition-based algorithm, i.e., MOEA/D.
Journal ArticleDOI
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
TL;DR: An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro genetic Algorithm mGA to form the proposed Modified micro Genetic Algorithm MmGA to improve its convergence rate towards the pareto optimal solutions.
Proceedings ArticleDOI
An archive based particle swarm optimisation for feature selection in classification
Bing Xue,A. K. Qin,Mengjie Zhang +2 more
TL;DR: A new PSO based feature selection approach, which introduces an external archive to store promising solutions obtained during the search process to guide the swarm to search for an optimal feature subset with the lowest classification error rate and a smaller number of features.
Journal ArticleDOI
Performance improvement of energy consumption, passenger time and robustness in metro systems: A multi-objective timetable optimization approach
TL;DR: A multi-objective integer programming model which integrates the energy consumption, the passenger waiting time, and the robustness by optimizing departure and arrival time of trains at each station is developed.
Journal ArticleDOI
MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation
TL;DR: A self-adaptive weight vector adjustment strategy based on chain segmentation strategy (CS) is proposed, which is firstly derived from the current population distribution to approximate the shape of the true Pareto front of the multi-objective problem.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Journal ArticleDOI
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Journal ArticleDOI
Muiltiobjective optimization using nondominated sorting in genetic algorithms
N. Srinivas,Kalyanmoy Deb +1 more
TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
Book
Evolutionary algorithms for solving multi-objective problems
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
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Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
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