Cooperative learning for radial basis function networks using particle swarm optimization

Alexandridis, Alex; Chondrodima, Eva; Sarimveis, Haralambos

doi:10.1016/j.asoc.2016.08.032

Cited by 59 publications

(29 citation statements)

References 35 publications

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“…PSO can also be hybridized by combining with adaptive crossover and mutation rates [30] or by combining PSO with ant colony optimization (ACO) for convex and non-convex economic load dispatch (ELD) problem of a small scale thermal power system [31]. Also, a cooperative PSO (CPSO) is applied for solving function approximation and classification problems with improved accuracy [32]. This algorithm uses two swarms for the same problem (dismantled into two parts) and both the swarms work in a cooperative manner to achieve an improved solution.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

2017

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This paper proposes a novel hybrid teaching-learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teachinglearning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after the teacher phase in TLBO with the best half of the population obtained after PSO. The population so obtained is used subsequently in learner phase of TLBO. To validate the proposed algorithm, five constrained benchmark functions are considered to prove its robustness and efficiency. The proposed algorithm is applied to synthesize four-bar linkage for prescribed path. It is found that the HTLPSO performs better than other single nature-inspired algorithms for path synthesis problem in mechanism theory. Hence, HTLPSO may prove to be an important tool for mechanism design to follow the prescribed path.

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Section: Introductionmentioning

confidence: 99%

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

2017

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“…Computational intelligence (CI) is the scientific domain that uses nature-inspired computational methodologies in order to cope with problems for which conventional mathematical reasoning and modelling can be inadequate, due to the complexity, the uncertainty, or the stochastic nature of them [16]. Genetic algorithms and the particle swarm optimization algorithm are considered to be among the CI methods that are most widely used for optimization [17][18][19][20][21][22].…”

Section: Computational Intelligence Methodsmentioning

confidence: 99%

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

et al. 2020

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The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.

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“…(2) novel learning schemes [76]; (3) hybrid methods with other algorithm [74]; and (4) local search operator [77].…”

Section: Related Workmentioning

confidence: 99%

“…For example, Wang et al [73] presented a new method to enhance learning speed and improved final performance, which directly tuned the Q-values to affect the action selection policy. Alex et al [74] presented a new evolutionary cooperative learning scheme that is able to solve function approximation and classification problems, improving accuracy and generalization capabilities. A new CS algorithm named snap-drift cuckoo search (SDCS) was presented by Hojjat et al [75].…”

Section: Introductionmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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Cooperative learning for radial basis function networks using particle swarm optimization

Cited by 59 publications

References 35 publications

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

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