Journal ArticleDOI
jMetal: A Java framework for multi-objective optimization
Juan J. Durillo,Antonio J. Nebro +1 more
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TLDR
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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Book ChapterDOI
Dynamic Multi-Objective Optimization with jMetal and Spark: A Case Study
José A. Cordero,Antonio J. Nebro,Cristóbal Barba-González,Juan J. Durillo,José García-Nieto,Ismael Navas-Delgado,José F. Aldana-Montes +6 more
TL;DR: This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems that combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum.
Journal ArticleDOI
A Simulation Optimisation Tool and Its Production/Inventory Control Application
TL;DR: The open-source software JSOptimizer is developed that can be used to optimise simulation models of complex engineering systems built with JaamSim and utilises the jMetal framework, a well-known and validated library of meta-heuristic optimisation algorithms.
Journal ArticleDOI
A Multi-Objective Artificial Bee Colony Algorithm Combined with a Local Search Method
TL;DR: Experimental results clearly demonstrate MOABCLS’s ability of finding a set of well converged and appropriately distributed non-dominated solutions, and the performance promotion by introducing the local search method.
Journal ArticleDOI
Optimization of Many Objective Pickup and Delivery Problem with Delay Time of Vehicle Using Memetic Decomposition Based Evolutionary Algorithm
Adeem Ali Anwar,Irfan Younas +1 more
TL;DR: A memetic I-DBEA (Improved Decomposition Based Evolutionary Algorithm) is proposed, which is basically the modification of an existing many-objective evolutionary algorithm called I- DBEA, which has significant advantages over several state-of-the-art algorithms in terms of the quality of the obtained solutions.
Journal Article
gpuMF: a framework for parallel hybrid metaheuristics on GPU with application to the minimisation of harmonics in multilevel inverters
TL;DR: The GPU metaheuristic framework (gpuMF) exploits the intrinsic parallelism found in metaheuristics and fully utilises the massively parallel architecture of GPUs.
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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