New modified bare-bones particle swarm optimization

Zhao, Xinchao; Liu, Huiping; Liu, Dongyue; Ai, Wenbao; Zuo, Xingquan

doi:10.1109/cec.2016.7743824

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…cognitive and social learning components: since the original PSO proposal the use of personal best-only PSO and social best-only PSO have been explored. Some of these variants are called Bare-Bones PSO (Kennedy, 2003; Krohling and Mendel, 2009; Zhao et al, 2016) and are based on the velocity term replacement and the use of Gaussian probability distributions to update particle positions. Also, some approaches (Jordehi and Jasni, 2013) propose different values for the cognitive and social constants, weighting differently both types of learning errors;…”

Section: Single-objective Nabi Metaheuristics: Overview Of Selected Tmentioning

confidence: 99%

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Oliveira

Pires

Boaventura‐Cunha

et al. 2020

Transactions of the Institute of Measurement and Control

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A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.

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Section: Single-objective Nabi Metaheuristics: Overview Of Selected Tmentioning

confidence: 99%

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Oliveira

Pires

Boaventura‐Cunha

et al. 2020

Transactions of the Institute of Measurement and Control

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An Atomic Retrospective Learning Bare Bone Particle Swarm Optimization

Zhou

Guo

Ke³

et al. 2023

Lecture Notes in Computer Science

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Comparative Study of Particle Swarm Optimization Algorithms in Solving Size, Topology, and Shape Optimization

Harsono

Prayogo

Prasetyo

et al. 2020

J. Phys.: Conf. Ser.

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This paper focuses on optimizing truss structures while propose best PSO variants. Truss optimization is one way to make the design efficient. There are three types of optimization, size optimization, shape optimization, and topology optimization. By combining size, shape and topology optimization, we can obtain the most efficient structure. Metaheuristics have the ability to solve this problem. Particle swarm optimization (PSO) is metaheuristic algorithm which is frequently used to solve many optimization problems. PSO mimics the behavior of flocking birds looking for food. But PSO has three parameters that can interfere with its performance, so this algorithm is not adaptive to diverse problems. Many PSO variants have been introduced to solve this problem, including linearly decreasing inertia weight particles swarm optimization (LDWPSO) and bare bones particles swarm optimization (BBPSO). The metaheuristic method is used to find the solution, while DSM s used to analyze the structure. A 10-bar truss structure and a 39-bar truss structure are considered as case studies. The result indicates that BBPSO beat other two algorithms in terms of best result, consistency, and convergence behaviour in both cases. LDWPSO took second place for the three categories, leaving PSO as the worst algorithm that tested.

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New modified bare-bones particle swarm optimization

Cited by 4 publications

References 20 publications

Review of nature and biologically inspired metaheuristics for greenhouse environment control

Review of nature and biologically inspired metaheuristics for greenhouse environment control

An Atomic Retrospective Learning Bare Bone Particle Swarm Optimization

Comparative Study of Particle Swarm Optimization Algorithms in Solving Size, Topology, and Shape Optimization

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