Genetic Learning Particle Swarm Optimization with Interlaced Ring Topology

Borowska, Bożena

doi:10.1007/978-3-030-50426-7_11

Cited by 7 publications

(7 citation statements)

References 31 publications

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“…According to Kennedy and Mendes [ 30 ], a proper topology significantly improves the exploration ability of PSO. Lin et al [ 31 ] and Borowska [ 32 ] indicated that the ring topology can help maintain swarm diversity and improve the algorithm’s adaptability. An approach based on multi-swarm structure was proposed by Chen et al [ 33 ] and Niu [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…To enhance diversity and improve the efficiency of PSO, Lin et al [ 31 ] merged PSO with genetic operators and also connected them with global learning strategy and ring topology. Learning strategy with genetic operators and interlaced ring topology was also proposed by Borowska [ 32 ]. To improve the searching process, Niu et al [ 50 ] recommended applying learning multi-swarm PSO based on a symbiosis.…”

Section: Introductionmentioning

confidence: 99%

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Learning Competitive Swarm Optimization

Borowska¹

2022

Entropy

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Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Competitive Swarm Optimization

Borowska¹

2022

Entropy

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“…While under the pressure of being faced with increasingly complex optimization problems in which derivative information is unreliable or unavailable, researchers gradually focus on the development of derivativefree optimization methods [2] and metaheuristic methods to address this issue. Followed by Glover's convention [3], modern metaheuristic algorithms such as simulated annealing (SA) [4], genetic algorithms (GA) [5,6], particle swarm optimization (PSO) [7], and ant colony optimization (ACO) [8] have been applied with good success in solving complex nonlinear optimization problems [9,10]. The popularity of these nature-inspired algorithms lies in their ease of implementation and the capability to obtain a solution close to the global optimum.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Level Search for Global Optimization: An Integrated Swarm Intelligence-Metamodelling Technique

Dong

Liu

et al. 2021

Applied Sciences

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Over the last decade, metaheuristic algorithms have emerged as a powerful paradigm for global optimization of multimodal functions formulated by nonlinear problems arising from various engineering subjects. However, numerical analyses of many complex engineering design problems may be performed using finite element method (FEM) or computational fluid dynamics (CFD), by which function evaluations of population-based algorithms are repetitively computed to seek a global optimum. It is noted that these simulations become computationally prohibitive for design optimization of complex structures. To efficiently and effectively address this class of problems, an adaptively integrated swarm intelligence-metamodelling (ASIM) technique enabling multi-level search and model management for the optimal solution is proposed in this paper. The developed technique comprises two steps: in the first step, a global-level exploration for near optimal solution is performed by adaptive swarm-intelligence algorithm, and in the second step, a local-level exploitation for the fine optimal solution is studied on adaptive metamodels, which are constructed by the multipoint approximation method (MAM). To demonstrate the superiority of the proposed technique over other methods, such as conventional MAM, particle swarm optimization, hybrid cuckoo search, and water cycle algorithm in terms of computational expense associated with solving complex optimization problems, one benchmark mathematical example and two real-world complex design problems are examined. In particular, the key factors responsible for the balance between exploration and exploitation are discussed as well.

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“…In [4], was proposed two conditions and rules to adjust the velocity, with the goal of to adapt the particles velocity periodically and independently. In [5], aiming to avoid premature convergence, was proposed a PSO with genetic learning, using an interlaced ring swarm topology and a flexible local search operator. In [6], was proposed a mechanism for updating the best particle in ring topology, aiming to avoid premature convergence, for application to multiobjective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A New GA-PSO Optimization Methodology with Fuzzy Adaptive Inertial Weight

Noronha

2021

Lecture Notes in Networks and Systems

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In this paper, a new optimization methodology is proposed concerning to hybridization involving Genetic Algorithm and Particle Swarm Optimization (GA-PSO) with fuzzy adaptive inertial weight. In order to optimize multimodal problems with fast and non-premature convergence, hybridization is performed by combining the desirable features of GA and PSO. However, a slow and premature convergence can still occur due to the inefficient trade-off between global and local search. To this end, in this paper, a Mamdani fuzzy system is used for parametric adaptation of the inertial weight of the PSO, since through the size of the inertial weight it is possible to define whether the search will occur global or local manner.

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Genetic Learning Particle Swarm Optimization with Interlaced Ring Topology

Cited by 7 publications

References 31 publications

Learning Competitive Swarm Optimization

Learning Competitive Swarm Optimization

Adaptive Multi-Level Search for Global Optimization: An Integrated Swarm Intelligence-Metamodelling Technique

A New GA-PSO Optimization Methodology with Fuzzy Adaptive Inertial Weight

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