A new hybrid particle swarm and simulated annealing stochastic optimization method

Javidrad, F.; Nazari, Mohammad Esmaeil

doi:10.1016/j.asoc.2017.07.023

Cited by 120 publications

(45 citation statements)

References 39 publications

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“…Tajbaksh et al [95] proposed the application of a hybrid PSO-SA to solve the Traveling Tournament Problem. Javidrad and Nazari [100] recently contributed a hybrid PSO-SA wherein SA contributes in updating the global best particle just when PSO does not show improvements in the performance of the global best particle, which may occur several times during the iteration cycles. The algorithm uses PSO in its initial phase to determine the global best and when there is no change in the global best in any particular cycle, passes the information on to the SA phase which iterates until a rejection takes place using the Metropolis criterion [101].…”

Section: Hybridization Of Pso Using Simulated Annealing (Sa)mentioning

confidence: 99%

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta¹,

Basak²,

Peters³

2018

Preprint

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state–of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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Section: Hybridization Of Pso Using Simulated Annealing (Sa)mentioning

confidence: 99%

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta¹,

Basak²,

Peters³

2018

Preprint

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“…In the present method, the best characteristics of the PSO, that has strong global-search ability, are combined with the good local search characteristics of the SA, to develop a novel hybrid algorithm; the proposed algorithm is called HyPSOSA. Other applications of hybrid PSO and SA algorithm can also be found in [14][15][16][17][18].…”

Section: The Proposed Hybrid Approachmentioning

confidence: 99%

A Novel Hybrid Approach for Optimizing the Localization of Wireless Sensor Networks

Lakhbab

2018

MATEC Web Conf.

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Wireless sensor networks are used for monitoring the environment and controlling the physical environment. Information gathered by the sensors is only useful if the positions of the sensors are known. One of the solutions for this problem is Global Positioning System (GPS). However, this approach is prohibitively costly; both in terms of hardware and power requirements. Localization is defined as finding the physical coordinates of a group of nodes. Localization is classified as an unconstrained optimization problem. In this work, we propose a new algorithm to tackle the problem of localization; the algorithm is based on a hybridization of Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Simulation results are given to illustrate the robustness and efficiency of the presented algorithm.

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“…The main advantage of PSO consists of its ability in exploitation while it may give weak result in exploring the solution space. In order to boost the performance of PSO, a series of hybrid PSO algorithms, which combine PSO with other performing search techniques, have been developed by literature so far (Sha & Hsu, 2006;Xia & Wu, 2006;Chen et al, 2013;Gao et al, 2014;Javidrad & Nazari, 2017). Notably, Arabameri and Salmasi (2013) demonstrated as the performance of PSO may be significantly improved if it is combined with a proper neighborhood search.…”

Section: Improving the Performance Of Psomentioning

confidence: 99%

Single machine batch processing problem with release dates to minimize total completion time

Beldar¹,

Costa²

2018

10.5267/j.ijiec

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A single machine batch processing problem with release dates to minimize the total completion time (1|rj,batch| j C  ) is investigated in this research. An original mixed integer linear programming (MILP) model is proposed to optimally solve the problem. Since the research problem at hand is shown to be NP-hard, several different meta-heuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) are used to solve the problem. To find the most performing heuristic optimization technique, a set of test cases ranging in size (small, medium, and large) are randomly generated and solved by the proposed meta-heuristic algorithms. An extended comparison analysis is carried out and the outperformance of a hybrid meta-heuristic technique properly combining PSO and genetic algorithm (PSO-GA) is statistically demonstrated.

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A new hybrid particle swarm and simulated annealing stochastic optimization method

Cited by 120 publications

References 39 publications

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

A Novel Hybrid Approach for Optimizing the Localization of Wireless Sensor Networks

Single machine batch processing problem with release dates to minimize total completion time

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