A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization

Nasir, .; Das, Swagatam; Maity, Dipankar; Sengupta, Shamik; Halder, Udit; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.ins.2012.04.028

Cited by 190 publications

(74 citation statements)

References 29 publications

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“…A PSO with a neighborhood operator, proposed by Suganthan, gradually increases the local neighborhood size in the search process [39]. Nasir et al proposed a dynamic neighborhood learning particle swarm optimizer (DNLPSO) [40]. In DNLPSO, the exemplar particle is selected from a neighborhood and the learner particle can learn from the historical information of its neighborhood.…”

Section: Improved Pso Based On Neighborhoodmentioning

confidence: 99%

Improving Particle Swarm Optimization Based on Neighborhood and Historical Memory for Training Multi-Layer Perceptron

2018

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Many optimization problems can be found in scientific and engineering fields. It is a challenge for researchers to design efficient algorithms to solve these optimization problems. The Particle swarm optimization (PSO) algorithm, which is inspired by the social behavior of bird flocks, is a global stochastic method. However, a monotonic and static learning model, which is applied for all particles, limits the exploration ability of PSO. To overcome the shortcomings, we propose an improving particle swarm optimization algorithm based on neighborhood and historical memory (PSONHM). In the proposed algorithm, every particle takes into account the experience of its neighbors and its competitors when updating its position. The crossover operation is employed to enhance the diversity of the population. Furthermore, a historical memory M w is used to generate new inertia weight with a parameter adaptation mechanism. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems on 30 dimensions. Finally, two classification problems are employed to investigate the efficiencies of PSONHM in training Multi-Layer Perceptron (MLP). The experimental results indicate that the proposed PSONHM can effectively solve the global optimization problems.

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Section: Improved Pso Based On Neighborhoodmentioning

confidence: 99%

Improving Particle Swarm Optimization Based on Neighborhood and Historical Memory for Training Multi-Layer Perceptron

2018

Information

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“…The widely used optimizers are inspired by nature phenomena, which include genetic algorithm (GA) [25], evolution programming (EP) [26,27], evolution strategy (ES) [28,29], differential evolution (DE) [6,30], ant colony optimization (ACO) [31], particle swarm optimization (PSO) [32][33][34][35][36][37], bacterial foraging optimization (BFO) [38], simulated annealing (SA) [39], tabu search (TS) [40], harmony search (HS) [35,36,40], etc. These optimizers facilitated research into the optimization of the subproblems.…”

Section: Q3mentioning

confidence: 99%

Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation

Sun

Yang

et al. 2015

Applied Soft Computing

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“…The whole population is divided into a large number of sub-swarms, these sub-swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the particles in the whole swarm. Zhao, Liang, Suganthan, Nasir and Li proposed also other sophisticated topologies in Zhao et al (2011), Liang and Suganthan (2006), Nasir et al (2012) and Li et al (2011).…”

Section: Dynamic Topologies With Regular Structurementioning

confidence: 99%

Dynamic cluster in particle swarm optimization algorithm

Dor

Lemoine²,

Clerc

et al. 2014

Nat Comput

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Particle swarm optimization is an optimization method based on a simulated social behavior displayed by artificial particles in a swarm, inspired from bird flocks and fish schools. An underlying component that influences the exchange of information between particles in a swarm, is its topological structure. Therefore, this property has a great influence on the comportment of the optimization method. In this study, we propose DCluster: a dynamic topology, based on a combination of two well-known topologies viz. Four-cluster and Fitness. The proposed topology is analyzed, and compared to six other topologies used in the standard PSO algorithm using a set of benchmark test functions and several well-known constrained and unconstrained engineering design problems. Our comparisons demonstrate that DCluster outperforms the other tested topologies and leads to satisfactory performance while avoiding the problem of premature convergence.

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A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization

Cited by 190 publications

References 29 publications

Improving Particle Swarm Optimization Based on Neighborhood and Historical Memory for Training Multi-Layer Perceptron

Improving Particle Swarm Optimization Based on Neighborhood and Historical Memory for Training Multi-Layer Perceptron

Cooperative differential evolution with fast variable interdependence learning and cross-cluster mutation

Dynamic cluster in particle swarm optimization algorithm

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