Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem

Zhang, Xin; Li, Xiang Tao; Yin, Ming

doi:10.1504/ijbic.2019.098403

Cited by 6 publications

(9 citation statements)

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“…These two algorithms can utilize each other's advantages to complement their shortcomings, thus balancing the quality of solution and the computation consumption. Zhang et al [85] devised a hybridization of CS and covariance matrix adaption evolution strategy (CMA_ES) to improve performance for the different optimization problems. Computational results demonstrate that the proposed algorithm outperforms other competitor algorithms.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…To date, more than a thousand different species of birds are in existence in nature [ 35 ]. For most of these species, the female birds lay eggs in nests they have built themselves [ 36 ].…”

Section: Standard Cuckoo Search (Cs)mentioning

confidence: 99%

An Innovative Excited‐ACS‐IDGWO Algorithm for Optimal Biomedical Data Feature Selection

Segera

Mbuthia

Nyete

2020

BioMed Research International

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Finding an optimal set of discriminative features is still a crucial but challenging task in biomedical science. The complexity of the task is intensified when any of the two scenarios arise: a highly dimensioned dataset and a small sample-sized dataset. The first scenario poses a big challenge to existing machine learning approaches since the search space for identifying the most relevant feature subset is so diverse to be explored quickly while utilizing minimal computational resources. On the other hand, the second aspect poses a challenge of too few samples to learn from. Though many hybrid metaheuristic approaches (i.e., combining multiple search algorithms) have been proposed in the literature to address these challenges with very attractive performance compared to their counterpart standard standalone metaheuristics, more superior hybrid approaches can be achieved if the individual metaheuristics within the proposed hybrid algorithms are improved prior to the hybridization. Motivated by this, we propose a new hybrid Excited- (E-) Adaptive Cuckoo Search- (ACS-) Intensification Dedicated Grey Wolf Optimization (IDGWO), i.e., EACSIDGWO. EACSIDGWO is an algorithm where the step size of ACS and the nonlinear control strategy of parameter a→ of the IDGWO are innovatively made adaptive via the concept of the complete voltage and current responses of a direct current (DC) excited resistor-capacitor (RC) circuit. Since the population has a higher diversity at early stages of the proposed EACSIDGWO algorithm, both the ACS and IDGWO are jointly involved in local exploitation. On the other hand, to enhance mature convergence at latter stages of the proposed algorithm, the role of ACS is switched to global exploration while the IDGWO is still left conducting the local exploitation. To prove that the proposed algorithm is superior in providing a good learning from fewer instances and an optimal feature selection from information-rich biomedical data, all these while maintaining a high classification accuracy of the data, the EACSIDGWO is employed to solve the feature selection problem. The EACSIDGWO as a feature selector is tested on six standard biomedical datasets from the University of California at Irvine (UCI) repository. The experimental results are compared with the state-of-the-art feature selection techniques, including binary ant-colony optimization (BACO), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), and extended binary cuckoo search algorithm (EBCSA). These results reveal that the EACSIDGWO has comprehensive superiority in tackling the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. Furthermore, the superiority of the proposed algorithm is proved via various numerical techniques like ranking methods and statistical analysis.

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“…2, the LEACH protocol also has problems such as clustering unevenness, cluster head election randomness and single-hop within the cluster. Domestic and foreign scholars have proposed swarm intelligence algorithm [27][28][29] and intelligent optimization algorithm [30,31] to optimization the performance of LEACH, and achieved corresponding results. Cui et al [32] proposed a bat algorithm (BA) [33,34] combined with centroid strategy to optimize the LEACH protocol.…”

Section: Sink Nodementioning

confidence: 99%

A many-objective optimization WSN energy balance model

2020

KSII TIIS

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Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

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Hybrid cuckoo search algorithm with covariance matrix adaption evolution strategy for global optimisation problem

Cited by 6 publications

References 0 publications

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

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

An Innovative Excited‐ACS‐IDGWO Algorithm for Optimal Biomedical Data Feature Selection

A many-objective optimization WSN energy balance model

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