Multi-objective Local Search Based on Decomposition

Derbel, Bilel; Liefooghe, Arnaud; Zhang, Qingfu; Aguirre, Hernán; Tanaka, Kiyoshi

doi:10.1007/978-3-319-45823-6_40

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…It decomposes a multi-objective optimisation problem (MOP) into a number of singleobjective optimisation sub-problems on the basis of a set of weights (or called weight vectors) and then uses a search heuristic to optimise these sub-problems simultaneously and cooperatively. Compared with conventional Pareto-based EMO, decomposition-based EMO has clear strengths, e.g., providing high selection pressure toward the Pareto front [1], being easy to work with local search operators [2][3][4], owning high search ability for combinatorial MOPs [5][6][7][8], and being capable of dealing with MOPs with many objectives [9][10][11][12] and MOPs with a complicated Pareto set [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

Yao

2020

Evolutionary Computation

126

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The quality of solution sets generated by decomposition-based evolutionary multiobjective optimisation (EMO) algorithms depends heavily on the consistency between a given problem's Pareto front shape and the specified weights' distribution. A set of weights distributed uniformly in a simplex often lead to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may fail on other Pareto front shapes. It is an open problem on how to specify a set of appropriate weights without the information of the problem's Pareto front beforehand. In this paper, we propose an approach to adapt the weights during the evolutionary process (called AdaW). AdaW progressively seeks a suitable distribution of weights for the given problem by elaborating five parts in the weight adaptation -weight generation, weight addition, weight deletion, archive maintenance, and weight update frequency. Experimental results have shown the effectiveness of the proposed approach. AdaW works well for Pareto fronts with very different shapes: 1) the simplex-like, 2) the inverted simplex-like, 3) the highly nonlinear, 4) the disconnect, 5) the degenerated, 6) the badly-scaled, and 7) the high-dimensional.

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Section: Introductionmentioning

confidence: 99%

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

Yao

2020

Evolutionary Computation

126

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“…Derbel et al [2] investigate the hybridization of single-objective local search move strategies within the MOEA/D framework. Besides, the multiobjective memetic algorithm based on decomposition (MOMAD) proposed by Ke et al [7] is one of the state-of-the-art algorithms for MCOPs.…”

Section: Related Work and Positioningmentioning

confidence: 99%

Using Parallel Strategies to Speed up Pareto Local Search

Shi

Zhang

Derbel

et al. 2017

Lecture Notes in Computer Science

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Abstract. Pareto Local Search (PLS) is a basic building block in many state-of-the-art multiobjective combinatorial optimization algorithms. However, the basic PLS requires a long time to find high-quality solutions. In this paper, we propose and investigate several parallel strategies to speed up PLS. These strategies are based on a parallel multi-search framework. In our experiments, we investigate the performances of different parallel variants of PLS on the multiobjective unconstrained binary quadratic programming problem. Each PLS variant is a combination of the proposed parallel strategies. The experimental results show that the proposed approaches can significantly speed up PLS while maintaining about the same solution quality. In addition, we introduce a new way to visualize the search process of PLS on twoobjective problems, which is helpful to understand the behaviors of PLS algorithms.

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“…However in [12] the decomposition is based on cone separation and it only can be applied to the problems with not more than three objectives. Derbel et al [13] combined the single-objective local search move strategies with the MOEA/D framework to optimize the bi-objective traveling salesman problems. Ke et al [14] proposed the Multiobjective Memetic Algorithm based on Decomposition (MOMAD), which is one of the state-of-the-art algorithms for MCOPs.…”

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confidence: 99%

PPLS/D: Parallel Pareto Local Search Based on Decomposition

Shi

Zhang

Sun

2020

IEEE Trans. Cybern.

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Pareto Local Search (PLS) is a basic building block in many metaheuristics for Multiobjective Combinatorial Optimization Problem (MCOP). In this paper, an enhanced PLS variant called Parallel Pareto Local Search based on Decomposition (PPLS/D) is proposed. PPLS/D improves the efficiency of PLS using the techniques of parallel computation and problem decomposition. It decomposes the original search space into L subregions and executes L parallel processes searching in these subregions simultaneously. Inside each subregion, the PPLS/D process is guided by a unique scalar objective function. PPLS/D differs from the well-known Two Phase Pareto Local Search (2PPLS) in that it uses the scalar objective function to guide every move of the PLS procedure in a fine-grained manner. In the experimental studies, PPLS/D is compared against the basic PLS and a recently proposed PLS variant on the multiobjective Unconstrained Binary Quadratic Programming problems (mUBQPs) and the multiobjective Traveling Salesman Problems (mTSPs) with at most four objectives. The experimental results show that, no matter whether the initial solutions are randomly generated or generated by heuristic methods, PPLS/D always performs significantly better than the other two PLS variants.Jialong Shi is with the

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Multi-objective Local Search Based on Decomposition

Cited by 8 publications

References 10 publications

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

Using Parallel Strategies to Speed up Pareto Local Search

PPLS/D: Parallel Pareto Local Search Based on Decomposition

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