Biogeography-based learning particle swarm optimization

Xu, Chunxiao; Tianfield, Huaglory; Mei, Congli; Du, Wenli; Liu, Guohai

doi:10.1007/s00500-016-2307-7

Cited by 179 publications

(54 citation statements)

References 37 publications

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“…The performance of MCSWOA was further verified by some advanced non−WOA variants. Thirteen algorithms consisting of BLPSO [43], CLPSO [44], CSO [45], DBBO [46], DE/BBO [47], GOTLBO [14], IJAYA [17], LETLBO [48], MABC [49], ODE [50], SATLBO [15], SLPSO [51], and TLABC [24] were employed for comparison in this subsection. The result of Wilcoxon's rank sum test tabulated in Table 12 shows that MCSWOA performed very competitively and outperformed all of the other 13 algorithms on 9 cases except Case 4, on which MCSWOA was surpassed by ODE and DBBO, and tied by TLABC.…”

Section: Comparison With Advanced Non−woa Variantsmentioning

confidence: 99%

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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Extracting accurate values for involved unknown parameters of solar photovoltaic (PV) models is very important for modeling PV systems. In recent years, the use of metaheuristic algorithms for this problem tends to be more popular and vibrant due to their efficacy in solving highly nonlinear multimodal optimization problems. The whale optimization algorithm (WOA) is a relatively new and competitive metaheuristic algorithm. In this paper, an improved variant of WOA referred to as MCSWOA, is proposed to the parameter extraction of PV models. In MCSWOA, three improved components are integrated together: (i) Two modified search strategies named WOA/rand/1 and WOA/current-to-best/1 inspired by differential evolution are designed to balance the exploration and exploitation; (ii) a crossover operator based on the above modified search strategies is introduced to meet the search-oriented requirements of different dimensions; and (iii) a selection operator instead of the “generate-and-go” operator used in the original WOA is employed to prevent the population quality getting worse and thus to guarantee the consistency of evolutionary direction. The proposed MCSWOA is applied to five PV types. Both single diode and double diode models are used to model these five PV types. The good performance of MCSWOA is verified by various algorithms.

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Section: Comparison With Advanced Non−woa Variantsmentioning

confidence: 99%

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

et al. 2019

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“…Adaptively decreasing cognitive parameter and increasing social parameter were employed to balance between exploitation and exploration. Chen et al [18] proposed a biogeography-based learning PSO (BLPSO) model to further enhance CLPSO. Both CLPSO and BLPSO performed velocity updating of each particle using the personal best solutions of different exemplar particles for different dimensions.…”

Section: Skin Cancer Detectionmentioning

confidence: 99%

Intelligent skin cancer detection using enhanced particle swarm optimization

Tan

Zhang

Neoh

et al. 2018

Knowledge-Based Systems

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In this research, we undertake intelligent skin cancer diagnosis based on dermoscopic images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature optimization. Since the identification of the most significant discriminative characteristics of the benign and malignant skin lesions plays an important role in robust skin cancer detection, the proposed PSO algorithm is employed for feature optimization. It incorporates not only subswarms, local and global food and enemy signals, attraction and flee operations, and mutation-based local exploitation, but also diverse matrix representations to mitigate premature convergence of the original PSO algorithm. Specifically, two remote swarm leaders, which show similar fitness but low position proximity, are used to lead the subswarm-based search and to enable the exploration of more distinctive search regions. Modified velocity updating strategies are also proposed to enable the particles to follow multiple swarm leaders and avoid the local and global worst individuals, partially (i.e. in randomly selected sub-dimensions) and fully (in every dimension), with an attempt to search for global optima. Probability distribution and dynamic matrix representations are used to diversify the search process. Evaluated with multiple skin lesion and UCI databases and diverse unimodal and multimodal benchmark functions, the proposed PSO variant shows a superior performance over those of other advanced and classical search methods for identifying discriminative features that facilitate benign and malignant lesion classification as well as for solving diverse optimization problems with different landscapes. The Wilcoxon rank sum test is adopted to further ascertain superiority of the proposed algorithm over other methods statistically.

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“…In the recent decades, nature-inspired meta-heuristic algorithms (MHAs) have emerged as powerful optimization tools for solving GOPs. Such MHAs include genetic algorithm (GA) [4], differential evolution (DE) [5,6], particle swarm optimization (PSO) [7,8], artificial bee colony (ABC) [9,10], biogeography-based optimization (BBO) [11,12], teaching-learning-based optimization (TLBO) [13,14], and artificial raindrop algorithm (ARA) [15].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Tianfield

2019

Swarm and Evolutionary Computation

Self Cite

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Artificial bee colony algorithm (ABC) has attracted wide attention in the recent decade. Although ABC algorithms can achieve good performance on separable problems by optimizing each variable independently, their performances on complex non-separable problems are still unsatisfactory. In this paper, through incorporating multiple differential search strategies and a self-adaptive mechanism within the framework of ABC, we propose a new ABC algorithm, called self-adaptive differential artificial bee colony (sdABC) algorithm. By means of differential search strategies, more variables will be updated each time based on the combination of mutation and crossover. Thus, sdABC has much enhanced ability for solving complex non-separable problems. Our proposed sdABC algorithm is evaluated on 28 benchmarks functions, including both common separable problems and complex non-separable CEC2015 functions. The experimental results show that sdABC can achieve much more desirable results compared with previous ABC algorithms on both separable and non-separable functions. Furthermore, the performance of our sdABC is also very competitive compared with well-established differential evolution and other meta-heuristic algorithms.

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Biogeography-based learning particle swarm optimization

Cited by 179 publications

References 37 publications

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

Intelligent skin cancer detection using enhanced particle swarm optimization

Self-adaptive differential artificial bee colony algorithm for global optimization problems

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