Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles

Zhu, Minghui; Chu, Shu-Chuan; Yang, Qingyong; Li, Wei; Pan, Jeng-Shyang

doi:10.1155/2021/5526127

Cited by 13 publications

(13 citation statements)

References 49 publications

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“…In this experiment, MPPE was compared with PPE, PSO, SCA, and MCSCA [53]. In the search process, all algorithms find their current optimal solutions through each iteration.…”

Section: Mppe Applicating In Iot Electric Bus Schedulingmentioning

confidence: 99%

Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

Zhu

Yan

Pan

et al. 2022

Wireless Communications and Mobile Computing

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The Phasmatodea population evolution algorithm (PPE) is a novel metaheuristic algorithm proposed in recent years, which simulates the evolutionary trend of stick insect population. In this article, a multigroup-based Phasmatodea population evolution algorithm with mutistrategy (MPPE) is proposed to further improve the overall performance of PPE. During the initialization period, the stick insect population is divided into multiple groups, and the step factor of the flower pollen algorithm is introduced into the population growth model of no more than half of the groups. This makes the population evolution trend diversified and prevents the algorithm from falling into the local optimal solution to a certain extent. In terms of intergroup communication, two communication strategies are adopted to mutate and replace the inferior particles, respectively, which improves the convergence speed and search ability of the algorithm. In the MPPE performance test, we compared it with PPE, five standard algorithms, and other parallel algorithms in CEC 2013 Benchmark Suite. Finally, this algorithm is applied to the IoT based electric bus scheduling for urban waterlogging situation, and the excellent performance of MPPE is verified comprehensively.

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“…In this experiment, MPPE was compared with PPE, PSO, SCA, and MCSCA [53]. In the search process, all algorithms find their current optimal solutions through each iteration.…”

Section: Mppe Applicating In Iot Electric Bus Schedulingmentioning

confidence: 99%

Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

Zhu

Yan

Pan

et al. 2022

Wireless Communications and Mobile Computing

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“…en, many researchers improve the parallel method based on Chang's theory. For example, Yang et al [31] and Zhu et al [19] proposed a parallel strategy of multiple groups and multiple strategies. Each population will update according to its own communication strategy.…”

Section: Parallel Strategymentioning

confidence: 99%

“…ese algorithms have been successfully applied to different fields of medical industry [17], image processing [18], transportation [19], and the like. However, according to the No Free Lunch eorem (NFL) [20], there is no metaheuristic algorithm suitable for handling all types of optimization problems.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Wang

et al. 2021

Journal of Advanced Transportation

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Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

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“…5 and |E| ≥ 0.5 then Update the vector X i using Equations ( 4)-( 6); end else if r ≥ 0.5 and |E| < 0.5 then Update the vector X i using Equation ( 7); end else if r < 0.5 and E |≥ 0.5 Update the vector X i using Equations ( 8)- (11); end else if r < 0.5and |E| < 0.5 Update the vector X i using Equations ( 12) and ( 13); end end end Optimal neighborhood disturbance using Equations ( 27) and ( 28…”

Section: Algorithm 1: Cshho Algorithmmentioning

confidence: 99%

“…Heuristic algorithms are dedicated to customizing algorithms through intuitive experience and problem information, but are often difficult to generalize due to their specialized nature. Compared to these two algorithms, meta-heuristic algorithms are more general and do not require deep adaptation to the problem, and although they do not guarantee optimal solutions, they can generally obtain optimal solutions under acceptable spatial and temporal conditions, although the degree of deviation from the optimal solution is difficult to estimate [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Harris Hawks Optimization with Multi-Strategy Search and Application

et al. 2021

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The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability.

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Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles

Cited by 13 publications

References 49 publications

Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Harris Hawks Optimization with Multi-Strategy Search and Application

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