Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems

Gong, Dunwei; Sun, Jing; Ji, Xinfang

doi:10.1016/j.ins.2013.01.020

Cited by 106 publications

(28 citation statements)

References 25 publications

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“…According to the timing of integrating the preference information into the optimizing process, three classes of algorithms can be defined [Evans 1984;Jaimes et al 2011a]: -A priori algorithms (selection before search): The preference information is set up before the search, and it will guide the population to converge to a subset of the PF [Auger et al 2009;Qiu et al 2012;]. -Interactive algorithms (selection during search): The optimization process asks the decision makers (DMs) for the preference information interactively to direct the search to the region of interest, which is a subset of the PF [Deb and Chaudhuri 2005;Deb et al 2006;Deb and Kumar 2007;Thiele et al 2009;Jaimes et al 2011b;Gong et al 2013a]. -A posteriori algorithms (selection after search): The preference information is introduced after running MaOEAs and obtaining a solution set that approximates the real PF [Purshouse et al 2011;Wang et al , 2013aWang et al , 2013c.…”

Section: Preference-based Approachmentioning

confidence: 99%

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Many-Objective Evolutionary Algorithms

et al. 2015

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Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.

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Section: Preference-based Approachmentioning

confidence: 99%

“…The algorithm is tested on DTLZ functions with up to 10 objectives. Gong et al [2013a] proposed an interactive evolutionary algorithm (IEA) based on the preference polyhedron theory. During the search process, the DM periodically obtains a nondominated solution set and chooses the most preferred one.…”

Section: Reference Directionmentioning

confidence: 99%

Many-Objective Evolutionary Algorithms

et al. 2015

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“…In this paper, an uncertainty factor ε is used to transform five popular precise functions, KUR, ZDT1, ZDT3, ZDT4 [2] and DTLZ1 [15], into interval multi-objective functions. Without loss of generality, this paper considers minimal optimization problems.…”

Section: Experiments and Analysismentioning

confidence: 99%

An Improved PSO Algorithm for Interval Multi-Objective Optimization Systems

Zhang

Gong

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYConsidering an uncertain multi-objective optimization system with interval coefficients, this letter proposes an interval multiobjective particle swarm optimization algorithm. In order to improve its performance, a crowding distance measure based on the distance and the overlap degree of intervals, and a method of updating the archive based on the acceptance coefficient of decision-maker, are employed. Finally, results show that our algorithm is capable of generating excellent approximation of the true Pareto front.

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“…They exist in many domains, such as scheduling [1,2], image processing [3][4][5][6], feature selection [7][8][9] and detection [10], path planning [11,12], feature selection [13], cyber-physical social system [14,15], texture discrimination [16], saliency detection [17], classification [18,19], object extraction [20], shape design [21], big data and large-scale optimization [22,23], multi-objective optimization [24], knapsack problem [25][26][27], fault diagnosis [28][29][30], and test-sheet composition [31]. Metaheuristic algorithms [32], a theoretical tool, are based on nature-inspired ideas, which have been extensively used to solve highly non-linear complex multi-objective optimization problems [33][34][35]. Several popular metaheuristics with a stochastic nature are compared in some studies [36][37][38] with deterministic Lipschitz methods by using operational zones.…”

Section: Introductionmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems

Cited by 106 publications

References 25 publications

Many-Objective Evolutionary Algorithms

Many-Objective Evolutionary Algorithms

An Improved PSO Algorithm for Interval Multi-Objective Optimization Systems

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

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