Two-phase multi-swarm PSO and the dynamic vehicle routing problem

Okulewicz, Michał; Mańdziuk, Jacek

doi:10.1109/cihli.2014.7013391

Cited by 14 publications

(24 citation statements)

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“…The third focus of the paper is on the relevance of implementation of particular components of the optimization method (whether hybrid or pure) for its final efficacy. This research thread is a continuation of our previous works devoted to detailed analysis of this aspect in the case of pure (non-hybridized) PSO Okulewicz and Ma ńdziuk (2013); Okulewicz and Mandziuk (2014); Okulewicz andMa ńdziuk (2017, 2019) and Memetic Algorithm Ma ńdziuk and Żychowski (2016). Specifically, in Okulewicz and Mandziuk (2014); Okulewicz and Ma ńdziuk (2017) we analyzed a Two-Phase Multi-Swarm Particle Swarm Optimizer (2MPSO) solving the Dynamic Vehicle Routing Problem (DVRP) Ma ńdziuk (2019) with the aim of finding an optimal configuration of several optimization improvement techniques dedicated to solving dynamic optimization problems within the 2MPSO framework.…”

Section: Contributionmentioning

confidence: 84%

“…This research thread is a continuation of our previous works devoted to detailed analysis of this aspect in the case of pure (non-hybridized) PSO Okulewicz and Ma ńdziuk (2013); Okulewicz and Mandziuk (2014); Okulewicz andMa ńdziuk (2017, 2019) and Memetic Algorithm Ma ńdziuk and Żychowski (2016). Specifically, in Okulewicz and Mandziuk (2014); Okulewicz and Ma ńdziuk (2017) we analyzed a Two-Phase Multi-Swarm Particle Swarm Optimizer (2MPSO) solving the Dynamic Vehicle Routing Problem (DVRP) Ma ńdziuk (2019) with the aim of finding an optimal configuration of several optimization improvement techniques dedicated to solving dynamic optimization problems within the 2MPSO framework. One of the main conclusions was that strong results achieved by 2MPSO should be mainly attributed to the following three factors: generating initial solutions with a clustering heuristic, optimizing the requeststo-vehicle assignment with a metaheuristic approach, and direct passing of solutions obtained in the previous stage (times step) of the problem solving procedure to the next stage.…”

Section: Contributionmentioning

confidence: 84%

See 1 more Smart Citation

Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples

Okulewicz,

Zaborski,

Mańdziuk

2020

Preprint

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In this paper we enhance Generalized Self-Adapting Particle Swarm Optimization algorithm (GAPSO), initially introduced at the Parallel Problem Solving from Nature 2018 conference, and to investigate its properties. The research on GAPSO is underlined by the two following assumptions: (1) it is possible to achieve good performance of an optimization algorithm through utilization of all of the gathered samples, (2) the best performance can be accomplished by means of a combination of specialized sampling behaviors (Particle Swarm Optimization, Differential Evolution, and locally fitted square functions).From a software engineering point of view, GAPSO considers a standard Particle Swarm Optimization algorithm as an ideal starting point for creating a generalpurpose global optimization framework. Within this framework hybrid optimization algorithms are developed, and various additional techniques (like algorithm restart management or adaptation schemes) are tested.The paper introduces a new version of the algorithm, abbreviated as M-GAPSO. In comparison with the original GAPSO formulation it includes the following four features: a global restart management scheme, samples gathering within an R-Tree based index (archive/memory of samples), adaptation of a sampling behavior based on a global particle performance, and a specific approach to local search.The above-mentioned enhancements resulted in improved performance of M-GAPSO over GAPSO, observed on both COCO BBOB testbed and in the black-box optimization competition BBComp. Also, for lower dimensionality functions (up to 5D) results of M-GAPSO are better or comparable to the state-of-the art version of CMA-ES (namely the KL-BIPOP-CMA-ES algorithm presented at the GECCO 2017 conference).

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Section: Contributionmentioning

confidence: 84%

Section: Contributionmentioning

confidence: 84%

Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples

Okulewicz,

Zaborski,

Mańdziuk

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Particles from different swarms migrate regularly to others to maintain diversity. A different multi-swarm was introduced in [134], where the communication was performed only with the arrival of a new environment.…”

Section: Multiple Population Methodsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

439

195

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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“…Despite their effectiveness in solving numerous engineering and production problems [6,19,14,15], those algorithms quite quickly "forget" the shape of optimized function due to their limited memory. Therefore, they loose information which might be useful in accelerating the convergence of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

Zaborski

Okulewicz

Mańdziuk

2020

Foundations of Computing and Decision Sciences

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This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper.We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.

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Two-phase multi-swarm PSO and the dynamic vehicle routing problem

Cited by 14 publications

References 10 publications

Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples

Generalized Self-Adapting Particle Swarm Optimization algorithm with archive of samples

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

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