Artificial bee colony algorithm based on chaos local search operator

Wang, Xiang; Li, Zhiyong; Xu, Guoyi; Wang, Yan

doi:10.3724/sp.j.1087.2012.01033

Cited by 6 publications

(2 citation statements)

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“…Conversely, the local development of cosine chaos compensates for the shortcoming of slow convergence in sine chaotic global search, which improves the efficiency of the algorithm. Comparing the progeny solutions generated by sine and cosine in chaotic prediction, the greedy mechanism is introduced to select the optimal solution [27]. To sum up, the sine cosine chaotic crossover optimization is adopted to avoid premature algorithm, aiming at improving the accuracy and calculation speed.…”

Section: Whale Optimization Algorithm Based On Chaotic Sine Cosine Opmentioning

confidence: 99%

Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

Peng

Zhang³

et al. 2018

Energies

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With power consumption increasing in China, the CO2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future.

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Section: Whale Optimization Algorithm Based On Chaotic Sine Cosine Opmentioning

confidence: 99%

Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

Peng

Zhang³

et al. 2018

Energies

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“…They are relatively effective in solving small-scale TSP problems, but they cannot solve the computational problems caused by large-scale nodes. Researchers have been searching for more efficient algorithms based on swarm intelligence for years to advance to deeper areas [15]. In practice, the distribution of nodes in some graphs shows certain characteristics, for example, in some locations nodes are densely distributed and in the real world such as residential buildings, network endpoints, and so on.…”

mentioning

confidence: 99%

An Improved Algorithm for Multi-Clusters TSP Based on ACO

Lin,

She

2023

Preprint

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In order to improve the efficiency of ACO algorithm in solving TSP problems with multi-cluster features, concepts of domain and density are defined. Then an improved ACO algorithm named MC-ACO is proposed for TSP with multi-cluster feature. The basic principles and Strategies of MC-ACO algorithm are introduced. The releasing mechanism of pheromone is analyzed, and probability of the next node selection in the final domain is processed to distinguish the different behaviors of ants inside and outside the clusters. The progress of the MC-ACO algorithm involving the jump process between clusters is illustrated in detail. Finally, this work makes some simulation experiments for TSP with MC-ACO and ACS, the test results show that MC-ACO has obvious advantages over ACS algorithm in solving TSP problems which have multiple clusters and large scale of nodes.

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