Multi-objective particle swarm optimization based on cooperative hybrid strategy

Yu, Hui; Wang, Yujia; Xiao, Shanli

doi:10.1007/s10489-019-01496-3

Cited by 15 publications

(5 citation statements)

References 36 publications

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“…Furthermore, in CHSPSO, Yu et al 29 adopted a variety of group strategies and a dynamic clustering strategy to improve the convergence and diversity of the algorithm by using the selection strategies of the lottery probability to ensure the diversity of the population. However, despite the improved performance of the algorithm, the computational complexity increases at the same time.…”

Section: Research Backgroundmentioning

confidence: 99%

Novel multiobjective particle swarm optimization based on ranking and cyclic distance strategy

Liu

Wang

Song

et al. 2022

Int J of Intelligent Sys

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To effectively improve the convergence and diversity of the multiobjective particle swarm optimization (MOPSO), we proposed a novel MOPSO based on ranking and cyclic distance (RCDMOPSO) that comprehensively considers the spatial target and congestion information of particles.RCDMOPSO introduced a method namely global proportional ranking (GPR) which differs from nondominated ranking under the Pareto framework, and designed a novel external archive maintenance and the global selection strategies of learning sample by combining GPR with cyclic distance. In this paper, RCDMOPSO together with eight classic and state-of-the-art algorithms were examined on ZDT, UF, and DTLZ series to test functions. The results show that RCDMOPSO is highly competitive in achieving the objectives of both convergence and diversity. RCDMOPSO outperformed other popular algorithms such as MOPSOs and multiobjective genetic algorithms based on comprehensive performance evaluation indicators inverted generational distance and hypervolume, thus supporting that RCDMOPSO is an effective approach to tackle multiobjective optimization problems.

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Section: Research Backgroundmentioning

confidence: 99%

Novel multiobjective particle swarm optimization based on ranking and cyclic distance strategy

Liu

Wang

Song

et al. 2022

Int J of Intelligent Sys

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“…Numerous recent studies explore developing effective MOPSO with a focus on how to best supply searching via a combination of methods to enhance and obtain more optimal solutions. In a study by [ 26 ], four strategies—multi-population, dynamic clustering, solution life, and probability lottery—were used in conjunction with their MOPSO variant model. The study concluded that their MOPSO variant model was superior since it included more than one strategy while searching.…”

Section: Literature Reviewmentioning

confidence: 99%

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Albowarab

Zakaria

Abidin

2021

Sensors

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Various aspects of task execution load balancing of Internet of Things (IoTs) networks can be optimised using intelligent algorithms provided by software-defined networking (SDN). These load balancing aspects include makespan, energy consumption, and execution cost. While past studies have evaluated load balancing from one or two aspects, none has explored the possibility of simultaneously optimising all aspects, namely, reliability, energy, cost, and execution time. For the purposes of load balancing, implementing multi-objective optimisation (MOO) based on meta-heuristic searching algorithms requires assurances that the solution space will be thoroughly explored. Optimising load balancing provides not only decision makers with optimised solutions but a rich set of candidate solutions to choose from. Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation (MP) models; distance angle multi-objective particle swarm optimization (DAMP) and angle multi-objective particle swarm optimization (AMP). Unlike existing models that only use crowding distance as a criterion for solution selection, our MP models probabilistically combine both crowding distance and crowding angle. More specifically, we only selected solutions that had more than a 0.5 probability of higher crowding distance and higher angular distribution. In addition, binary variants of the approaches were generated based on transfer function, and they were denoted by binary DAMP (BDAMP) and binary AMP (BAMP). After using MOO mathematical functions to compare our models, BDAMP and BAMP, with state of the standard models, BMP, BDMP and BPSO, they were tested using the proposed load balancing model. Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO. Therefore, this study enables the incorporation of meta-heuristic in the management layer of cloud networks.

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“…The first phase is initializing the defined population based on the proposed particle encoding scheme. For two objective optimizations, the multi-objective PSO technique known as OMOPSO [26] is used in the subsequent phase.…”

Section: B Proposed Image Classification Algorithmmentioning

confidence: 99%

A Deep Learning Model Based on Multi-Objective Particle Swarm Optimization for Scene Classification in Unmanned Aerial Vehicles

Rajagopal¹,

Joshi

Ramachandran³

et al. 2020

IEEE Access

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Multi-objective particle swarm optimization based on cooperative hybrid strategy

Cited by 15 publications

References 36 publications

Novel multiobjective particle swarm optimization based on ranking and cyclic distance strategy

Novel multiobjective particle swarm optimization based on ranking and cyclic distance strategy

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

A Deep Learning Model Based on Multi-Objective Particle Swarm Optimization for Scene Classification in Unmanned Aerial Vehicles

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