Using Parallel Strategies to Speed up Pareto Local Search

Shi, Jialong; Zhang, Qingfu; Derbel, Bilel; Liefooghe, Arnaud; Vérel, Sebástien

doi:10.1007/978-3-319-68759-9_6

Cited by 8 publications

(26 citation statements)

References 17 publications

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“…Shi et al [6] proposed a diagram called "trajectory tree" to show the behavior of a PLS process. For a PLS process, the trajectory tree plots all the solutions that were ever accepted by the archive and the neighborhood relationship among them on the objective space.…”

Section: G Algorithm Behavior Investigationmentioning

confidence: 99%

“…In this paper, the proposed PPLS/D can handle more than two objectives by using the region decomposition method proposed by Liu et al [7]. • In [6], the scalar objective functions that guide the search are generated by the weighted sum approach, while in this paper the scalar objective functions are generated by the Tchebycheff approach. The advantages of using the Tchebycheff scalar objective function are discussed in Section III-A.…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of using the Tchebycheff scalar objective function are discussed in Section III-A. • In [6], several speed-up strategies are proposed for PLS, but the details of the strategies are not given. In this paper, a formal algorithm, PPLS/D, is proposed based on the strategies proposed in [6] and the aforementioned improvements.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the mUBQP and the mTSP (minimization problem) with two, three and four objectives are used as the test suites, and the algorithms are started from randomly generated solutions and high quality solutions. • In [6], there is no investigation about how the algorithm performance changes with different process numbers, while in this paper we analysis the influence of the process number. The rest of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: G Algorithm Behavior Investigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PPLS/D: Parallel Pareto Local Search Based on Decomposition

Shi

Zhang

Sun

2020

IEEE Trans. Cybern.

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“…On the other hand, in some recent papers [10,11], it is shown how to incorporate decomposition into PLS with the aim of bringing parallelism into the scene to enhance search performance. Inspired by the MOEA/D algorithm [12], Shi et al [10,11] proposed to use dierent scalar functions to guide a number of independent PLS processes in parallel towards dierent regions of the objective space. The archives obtained by dierent processes are merged together into one output archive at the end of the run.…”

Section: Introductionmentioning

confidence: 99%

Parallel pareto local search revisited

Shi

Zhang

Derbel

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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Pareto Local Search (PLS) is a simple, yet eective optimization approach dedicated to multi-objective combinatorial optimization. It can however suer from a high computational cost, especially when the size of the Pareto optimal set is relatively large. Recently, incorporating decomposition in PLS had revealed a high potential, not only in providing high-quality approximation sets, but also in speeding-up the search process. Using the bi-objective Unconstrained Binary Quadratic Programming (bUBQP) problem as an illustrative benchmark, we demonstrate some shortcomings in the resulting decomposition-guided Parallel Pareto Local Search (PPLS), and we propose to revisit the PPLS design accordingly. For instances with a priori unknown Pareto front shape, we show that a simple pre-processing technique to estimate the scale of the Pareto front can help PPLS to better balance the workload. Furthermore, we propose a simple technique to deal with the critically-important scalability issue raised by PPLS when deployed over a large number of computing nodes. Our investigations show that the revisited version of PPLS provides a consistent performance, suggesting that decomposition-guided PPLS can be further generalized in order to improve both parallel eciency and approximation quality.

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