An elite-guided hierarchical differential evolution algorithm

Zhong, Xuxu; Cheng, Peng

doi:10.1007/s10489-020-02091-7

Cited by 28 publications

(12 citation statements)

References 56 publications

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“…The evolutionary algorithm (EA) is an important branch of AI technology, and it is widely employed for solving complex optimization problems [24]. In recent years, many EAbased methodologies are widely developed and obtain promising performance in solving real-world optimization problems [25][26][27].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance

Tang

et al. 2022

Sensors

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In recent years, the unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the planning, design and maintenance of urban distributed photovoltaic arrays (UDPA). However, the existing studies rarely concern the UAV swarm scheduling problem when applied to remoting sensing in UDPA maintenance. In this study, a novel scheduling model and algorithm for UAV swarm remote sensing in UDPA maintenance are developed. Firstly, the UAV swarm scheduling tasks in UDPA maintenance are described as a large-scale global optimization (LSGO) problem, in which the constraints are defined as penalty functions. Secondly, an adaptive multiple variable-grouping optimization strategy including adaptive random grouping, UAV grouping and task grouping is developed. Finally, a novel evolutionary algorithm, namely cooperatively coevolving particle swarm optimization with adaptive multiple variable-grouping and context vector crossover/mutation strategies (CCPSO-mg-cvcm), is developed in order to effectively optimize the aforementioned UAV swarm scheduling model. The results of the case study show that the developed CCPSO-mg-cvcm significantly outperforms the existing algorithms, and the UAV swarm remote sensing in large-scale UDPA maintenance can be optimally scheduled by the developed methodology.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance

Tang

et al. 2022

Sensors

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“…2. The proposed algorithm is derived from SHADE, JADE and it might be assumed that SDIS is inherited (Awad et al, 2018), (Cheng et al, 2020), (Meng et al, 2020), (Yi et al, 2021), (Zhong and Cheng, 2021), (Kumar et al, 2022) (Zuo and Guo, 2022): HVB.…”

Section: State-of-the-art On Strategies Of Dealing With Infeasible So...mentioning

confidence: 99%

The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

Kononova¹,

Vermetten²,

Caraffini³

et al. 2022

Preprint

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We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple box constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours -in terms of performance, disruptiveness and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants on special test function f0 and BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem's dimensionality. Different Evolution is not at all special in this regard -there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the field of heuristic optimisation to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we call here a strategy of dealing with infeasible solutions. This component needs to be consistently (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on algorithm's performance in a wider sense and (c) included in the (automatic) algorithmic design. All of these should be done even for problems with box constraints.

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“…It is verified that DMMFO algorithm has better performance on 30 benchmark functions and 3 engineering optimization cases. Zhong and Cheng 19 proposed an improved DE algorithm (EHDE), which introduced two different strategies, and the performance of the EHDE algorithm is verified. Ahmed et al 20 proposed a knowledge-based artificial bee colony algorithm (K-ABC), which has achieved good results in inverse kinematics of robot and other complex constraint engineering problems.…”

Section: Introductionmentioning

confidence: 99%

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

Zhang

Song

et al. 2022

Advances in Mechanical Engineering

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To advance the calculation performance of the battle royale optimization algorithm (BRO), a hybrid improved BRO algorithm (HBC) is proposed in this paper. The level mechanism of the chicken swarm optimization algorithm (CSO) is integrated into the BRO algorithm to divide all into elite players and ordinary players, and the level relationship of different players is established. Then, an elite player update method of random exploring and directional update in a small range is proposed to improve the development ability. The update method of ordinary players improved, the update mechanism of elite random guidance is introduced to make full use of the excellent location information in the population. The performance verification experiment of the HBC algorithm is carried out on 20 benchmark functions and a practical project. Comparing with several other algorithms, the computational performance of the HBC algorithm is the best. Furthermore, the HBC algorithm is applied to solve the inverse kinematics of the 7R 6DOF robot. The experimental results show that the HBC algorithm effectively improves the average convergence accuracy and reduces the running time, compared with the BRO algorithm. This fully shows that the HBC algorithm is more competitive in stability, calculation accuracy, and speed.

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An elite-guided hierarchical differential evolution algorithm

Cited by 28 publications

References 56 publications

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance

Intelligent Scheduling Methodology for UAV Swarm Remote Sensing in Distributed Photovoltaic Array Maintenance

The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

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