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Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic
2002
Mathematics and Computers in Simulation
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In this paper, we propose a Pareto approach based on the hybridization of fuzzy logic (FL) and evolutionary algorithms (EAs) to solve the flexible job-shop scheduling problem (FJSP). ...
The integration of these two methodologies for the multi-objective optimization has become an increasing interest. ...
In a previous work, we have proposed an aggregative approach for solving multi-objective optimization problems (MOPs) based on the hybridization of fuzzy logic (FL) and evolutionary algorithms (EAs) [6 ...
doi:10.1016/s0378-4754(02)00019-8
fatcat:qg5hbppltfgx5c674mcnldh2fi
Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review
2012
Renewable & Sustainable Energy Reviews
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Therefore, in the present study, an overview of applied multi-objective methods by using evolutionary algorithms for hybrid renewable energy systems was proposed to help the present and future research ...
The result shows that there are a few studies about optimization of many objects in a hybrid system by these algorithms and the most popular applied methods are genetic algorithm and particle swarm optimization ...
The ultimate goal of a multi-objective optimization algorithm is to identify solutions in the Pareto optimal set. ...
doi:10.1016/j.rser.2012.02.071
fatcat:qwg5h55b4ramrf4hhe3zo7xz34
A hybrid method for solving multi-objective global optimization problems
2006
Journal of Global Optimization
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In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of optimums, which constitute the so called Pareto-optimal ...
The objective of this work is to compare the performance of a new hybrid method here proposed, with several well-known multi-objective evolutionary algorithms (MOEA). ...
Keywords Multi-objective optimization · Global pareto-optimal front · Evolutionary algorithms
Introduction The aim of global optimization (GO) is to find the best solution of decision models, in presence ...
doi:10.1007/s10898-006-9105-1
fatcat:wxj6xhyz5zdp7ks6n5zytqvvbe
Hybridisation of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimisation Problems
2009
Scientific Journal of Riga Technical University Computer Sciences
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Hybridisation of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimisation Problems The paper presents a taxonomic analysis of existing hybrid multi-objective evolutionary algorithms ...
Finally, a combination of the properties, which allows one to increase the approximation accuracy of the Pareto-optimal front at relatively low computational costs, is revealed. ...
A Unified View on the Hybrid Multi-Objective Evolutionary Algorithms In the literature, several hybrid multi-objective evolutionary algorithms can be found. ...
doi:10.2478/v10143-010-0001-2
fatcat:rwfjfvhzxvcphfhzpvqxkrmvli
A Novel Hybrid Evolutionary Algorithm for Solving Multi-Objective Optimization Problems
[chapter]
2012
Lecture Notes in Computer Science
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This paper applies an evolutionary optimization scheme , inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategies , to find approximate solutions for multiobjective ...
The desired control function may be subjected to severe changes over a period of time . In response to deficiency , the process of dispersal has been modified in the MOIWO . ...
In the former , a multi-objective optimizer may be applied one at a time with the goal of finding one single Pareto optimal solution . ...
doi:10.1007/978-3-642-31588-6_17
fatcat:kaqc473rkff4xhfabvsnirpygm
Microarray Probe Design Using ε-Multi-Objective Evolutionary Algorithms with Thermodynamic Criteria
[chapter]
2006
Lecture Notes in Computer Science
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Then the probe set for human paillomavrius (HPV) was found using -multi-objective evolutionary algorithm with thermodynamic fitness calculation. ...
First, the probe design for DNA microarray was formulated as a constrained multi-objective optimization task by investigating the characteristics of probe design. ...
The RIACT at Seoul National University provides research facilities for this study. The target gene and probe sequences are supplied by Biomedlab Co., Korea. ...
doi:10.1007/11732242_17
fatcat:wgkkza7i7jddtgtygfqw5wrgbi
Multi-objective Evolutionary Probe Design Based on Thermodynamic Criteria for HPV Detection
[chapter]
2004
Lecture Notes in Computer Science
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Since a multi-objective evolutionary algorithm can find multiple solutions at a time, we used thermodynamic criteria to choose the most suitable one. ...
We propose a multi-objective evolutionary approach, which is known to be suitable for this kind of optimization problem. ...
The Multi-objective Evolutionary Approach We try to find optimal probe sets using multi-objective evolutionary algorithm. ...
doi:10.1007/978-3-540-28633-2_78
fatcat:zvg2siwtbfaytjnsy35bx55goy
A Hybrid Coa-Dea Method For Solving Multi-Objective Problems
2015
International Journal on Computational Science & Applications
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So the multi-objective cuckoo optimization algorithm based on data envelopment analysis (DEA) is developed in this paper and it can gain the efficient Pareto frontiers. ...
The Cuckoo optimization algorithm (COA) is developed for solving single-objective problems and it cannot be used for solving multi-objective problems. ...
This hybrid method finds the efficient points using DEA method and gains the Pareto frontiers for multi-objective problems. The steps of hybrid COA_DEA algorithm 1. ...
doi:10.5121/ijcsa.2015.5405
fatcat:43j3g2opqvbo5fipmlfbcdhrr4
Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration
2015
Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15
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We therefore aim to exploit gradient information within an evolutionary-algorithmbased multi-objective optimization framework for DIR. ...
To assess its utility, we compare a state-of-the-art multi-objective evolutionary algorithm with three different hybrid versions thereof on several benchmark problems and two medical DIR problems. ...
BENCHMARK PROBLEMS We have selected a set of well-known benchmark problems in multi-objective evolutionary optimization, see Table 1. ...
doi:10.1145/2739480.2754719
dblp:conf/gecco/PirpiniaASHB15
fatcat:5yu7q4eupvda7ms7vaeksqrrau
Hybrid Model for Solving Multi-Objective Problems Using Evolutionary Algorithm and Tabu Search
[article]
2011
arXiv
pre-print
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of evolutionary algorithm. ...
This paper presents a new multi-objective hybrid model that makes cooperation between the strength of research of neighborhood methods presented by the tabu search (TS) and the important exploration capacity ...
STRENGTH PARETO EVOLUTIONARY ALGORITHM Evolutionary algorithms seem particularly suitable to solve multi-objective optimization problems because they deal simultaneously with a set of possible solutions ...
arXiv:1102.2984v1
fatcat:6wa5btybercr7fjsg5hpebeli4
Multi-objective optimization using metaheuristics: non-standard algorithms
2012
International Transactions in Operational Research
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In particular, we focus on non-evolutionary metaheuristics, hybrid multi-objective metaheuristics, parallel multi-objective optimization, and multi-objective optimization under uncertainty. ...
Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front ...
[Pareto Optimal Set] For a given MOPfðxÞ, the Pareto optimal set is defined as P Ã ¼ fx 2 Oj:9x 0 2 O;fðx 0 Þ%fðxÞg: [Pareto Front ] For a given MOPfðxÞ and its Pareto optimal set P Ã , the Pareto front ...
doi:10.1111/j.1475-3995.2011.00808.x
fatcat:wkcurdztanek5i5ptwfikqha3a
Promising cannabinoid-based therapies for Parkinson's disease: motor symptoms to neuroprotection
2015
Molecular Neurodegeneration
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In particular, we focus on non-evolutionary metaheuristics, hybrid multi-objective metaheuristics, parallel multi-objective optimization, and multi-objective optimization under uncertainty. ...
Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front ...
[Pareto Optimal Set] For a given MOPfðxÞ, the Pareto optimal set is defined as P Ã ¼ fx 2 Oj:9x 0 2 O;fðx 0 Þ%fðxÞg: [Pareto Front ] For a given MOPfðxÞ and its Pareto optimal set P Ã , the Pareto front ...
doi:10.1186/s13024-015-0012-0
pmid:25888232
pmcid:PMC4404240
fatcat:pejeg6cefbcdro7u4uot24pkxa
Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems
2009
2009 IEEE Congress on Evolutionary Computation
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Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-dominated solutions for over a decade. ...
The success of our approach on most of the test problems not only provides confidence but also stresses the importance of hybrid evolutionary algorithms in solving multi-objective optimization problems ...
INTRODUCTION Evolutionary multi-objective optimization (EMO) algorithms are playing a dominant role in solving problems with multiple conflicting objectives and obtaining a set of nondominated solutions ...
doi:10.1109/cec.2009.4983310
dblp:conf/cec/SindhyaSDM09
fatcat:bh4ihkv3rjcqvhm2oo2c4wufdi
Survey on Multi-Objective Evolutionary Algorithms
2019
Journal of Physics, Conference Series
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Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. ...
Finally several viewpoints for the future research of MOEA are presented. ...
[34] developed a KKT Proximity Measure (KKTPM) for estimating proximity of a solution from Pareto optimal set. The Pareto-dominated MOEAs are not suitable for many-objective optimization. ...
doi:10.1088/1742-6596/1288/1/012057
fatcat:jugqqqyonvhoflgkvs5xmdtjga
Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms
2006
Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06
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EMO algorithms try to find a set of well-distributed Pareto-optimal solutions with a wide range of objective values. ...
It is, however, very difficult for EMO algorithms to find a good solution set of a multiobjective combinatorial optimization problem with many decision variables and/or many objectives. ...
EMO algorithms are designed to find a set of well-distributed Pareto-optimal solutions with a wide range of objective values. ...
doi:10.1145/1143997.1144126
dblp:conf/gecco/IshibuchiNND06
fatcat:slxrb4odbng4jmfslim6w45p3m
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