A Modified Multi-objective Binary Particle Swarm Optimization Algorithm

Wang, Sheng; Ye, Wei; Fu, Xiping; Menhas, Muhammad Ilyas

doi:10.1007/978-3-642-21524-7_6

Cited by 7 publications

(11 citation statements)

References 12 publications

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“…The original particle swarm optimization (PSO) algorithm is best suited for an optimization of continuous problems, but several modifications [36,37] exist, which enable it to be used for discrete problems. In the case of multiple objectives which contradict each other, the PSO algorithm may be adapted to find a Pareto optimal set of solutions [38,39]. We used the Multi-objective particle swarm optimization (MOPSO) method proposed by Coello et.…”

Section: Imopso For Finding a Pareto Set Of Possible Service Distribu...mentioning

confidence: 99%

Method for Dynamic Service Orchestration in Fog Computing

et al. 2021

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Fog computing is meant to deal with the problems which cloud computing cannot solve alone. As the fog is closer to a user, it can improve some very important QoS characteristics, such as a latency and availability. One of the challenges in the fog architecture is heterogeneous constrained devices and the dynamic nature of the end devices, which requires a dynamic service orchestration to provide an efficient service placement inside the fog nodes. An optimization method is needed to ensure the required level of QoS while requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle of the whole IoT system. A two-stage multi-objective optimization method to find the best placement of services among available fog nodes is presented in this paper. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. Then, the analytical hierarchy process is used to choose the best service distribution according to the application-specific judgment matrix. An illustrative scenario with experimental results is presented to demonstrate characteristics of the proposed method.

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Section: Imopso For Finding a Pareto Set Of Possible Service Distribu...mentioning

confidence: 99%

Method for Dynamic Service Orchestration in Fog Computing

et al. 2021

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“…Multi-objective Problems (MOPs) are known to have many contradictory objectives where enhancing the result of one objective will have a negative impact on the other objectives involved. MOPSO attempts to effectively find a solution or a set of solutions that ensure a balance between all the involved objectives as is thoroughly discussed in [28][29][30][31][32][33][34][35][36]. The main differences between the SOPSO and the MOPSO algorithms are:…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…To avoid filling up the leaders archive, a crowding distance based on the non-dominated sorting genetic algorithm-II (NSGA-II) is used to decide which particles must remain in the archive [38,39]; and 4. A mutation operator is applied to a portion of the swarm to improve the exploration and search ability and to avoid premature convergence [34,35,37]. Using the mutation method allowed us to give up using a simulated annealing method used to enhance the SOPSO performance by dynamically varying the inertia weight value.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Particle Swarm Optimized Power Consumption of Trilateration

Al-Olimat¹,

Green²,

Alam³

et al. 2014

IJFCST

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Trilateration-based localization (TBL) has become a corner stone of modern technology. This study formulates the concern on how wireless sensor networks can take advantage of the computational intelligent techniques using both single- and multi-objective particle swarm optimization (PSO) with an overall aim of concurrently minimizing the required time for localization, minimizing energy consumed during localization, and maximizing the number of nodes fully localized through the adjustment of wireless sensor transmission ranges while using TBL process. A parameter-study of the applied PSO variants is performed, leading to results that show algorithmic improvements of up to 32% in the evaluated objectives.Comment: 19 Pages, 13 Figures, 10 Tables, Journa

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“…Our second contribution will be to add to the evidence [3] [7] that PSOs operating in a discrete search space may find good solutions more easily without a traditional velocity update rule, particularly for multi-objective problems, as evidenced in [8]. Velocity-free algorithms appear superior in this sense.…”

Section: Introductionmentioning

confidence: 99%

“…in which p t i is the best parameter position found at time t by particle i, g t the best position found by any particle, c 1 and c 2 are positive constants most often set to 2.0 [8], r 1 and r 2 are random numbers ∈ [0, 1], and w is a usually iteration-decreasing "inertia weight" that controls the extent to which new search directions are pursued. It is also usually recommended to clip the velocity to a maximum magnitude V max so that the search remains within useful bounds.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization

Wei¹,

Gorse²

2022

Preprint

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This paper extends boolean particle swarm optimization to a multi-objective setting, to our knowledge for the first time in the literature. Our proposed new boolean algorithm, MBOnvPSO, is notably simplified by the omission of a velocity update rule and has enhanced exploration ability due to the inclusion of a "noise" term in the position update rule that prevents particles being trapped in local optima. Our algorithm additionally makes use of an external archive to store non-dominated solutions and implements crowding distance to encourage solution diversity. In benchmark tests, MBOnvPSO produced high quality Pareto fronts, when compared to benchmarked alternatives, for all of the multi-objective test functions considered, with competitive performance in search spaces with up to 600 discrete dimensions. KeywordsBinary PSO • Boolean PSO • Multi-objective optimization • Velocity-free 2 Background and related work 2.1 Continuous, binary, and boolean PSO PSO was first introduced in Kennedy and Eberhart [4]. It is a nature-inspired population-based search algorithm modelled after the flocking behavior of birds and other animals. The PSO optimization process, for the ith particle, is

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A Modified Multi-objective Binary Particle Swarm Optimization Algorithm

Cited by 7 publications

References 12 publications

Method for Dynamic Service Orchestration in Fog Computing

Method for Dynamic Service Orchestration in Fog Computing

Particle Swarm Optimized Power Consumption of Trilateration

A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization

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