A novel hybrid clonal selection algorithm for the corridor allocation problem with irregular material handling positions

Liu, Juniqi; Zhang, Zeqiang; Gong, Juhua; Chen, Feng; Yin, Tao

doi:10.1016/j.cie.2022.108118

Cited by 7 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this implementation, antigens represent the optimization problem, while antibodies correspond to optimal solutions. Over years of development, the CSA has evolved into various variants, proving successful in applications such as pattern recognition [39], feature selection [40], combinational optimization [41], numerical optimization [42], and other fields [43]. Despite modifications in different versions of CSA, the fundamental processes remain consistent.…”

Section: Clonal Selection Algorithmmentioning

confidence: 99%

“…Some studies have begun extending parameters to boundaries initially established for PV cell/module parameter estimation [44][45][46]. The CSA, equipped with inherent parallel searching, selflearning, and memory mechanisms, has found numerous successful applications in letter recognition, numerical optimization, combinatorial optimization, and more [39][40][41][42]. However, as widely acknowledged, CSA faces challenges such as premature convergence and low accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

Zhang,

Yang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Converting solar energy to electricity efficiently requires an accurate model with well-estimated parameters in a photovoltaic (PV) system. However, identifying parameters for the PV model remains challenging attributed to the nonlinearity and multimodality inherent in diode circuit models. In this paper, we propose a dual-feedback adaptive clone selection algorithm with a golden sine search. The clonal selection algorithm (CSA), known for its inherent diversity maintenance ability, offers an advantage in handling multimodal optimization problems. The critical factor is the cloning operator, particularly the determination of clone size, crucial for balancing exploration and exploitation. To address the conflict between clone size and convergence speed, we introduce a dual feedback adaptive cloning strategy. This strategy considers both the fitness difference between the hypermutated population and the original population and the harmonic mean distance of the population. Additionally, to improve accuracy, we introduce an elite learning strategy based on the golden sine mechanism. The proposed algorithm's effectiveness is assessed through comprehensive experiments on PV cells and three PV modules, considering both single diode and double diode models, respectively. The experimental results demonstrate that the accuracy of parameter identification achieved by the proposed algorithm exceeds 99.9% for all three types of PV modules. Moreover, we validate the algorithm's performance using manufacturer's datasheets with varying irradiations and temperatures. All experiments consistently indicate the stability and effectiveness of the proposed method in accurately identifying the parameters of PV models.

show abstract

Section: Clonal Selection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

Zhang,

Yang,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In this section, we conduct an in-depth examination of the performance of MODBA, comparing it with other advanced algorithms from the literature that have been proven effective in solving line balancing and other discrete location-based optimisation problems. These include the non-dominated sorting genetic algorithm II (NSGA-II) [35], the multiobjective novel immune clonal algorithm (NICA-II) [37], the MOPSO algorithm [11], the improved gravitational search algorithm (GSA) [13], and the balanced-quantum inspired evolutionary algorithm (QEA) [14]. We perform a comprehensive analysis of the performances of different algorithms using three well-known multiobjective evaluation metrics: the number of Pareto solutions (NPS), inverted generational distance (IGD), and hypervolume (HV).…”

Section: Algorithm Performance Analysismentioning

confidence: 99%

Human–Robot Collaboration on a Disassembly-Line Balancing Problem with an Advanced Multiobjective Discrete Bees Algorithm

Shen,

Lu,

Sheng

et al. 2024

Symmetry

View full text Add to dashboard Cite

As resources become increasingly scarce and environmental demands grow, the recycling of products at the end of their lifecycle becomes crucial. Disassembly, as a key stage in the recycling process, plays a decisive role in the sustainability of the entire operation. Advances in automation technology and the integration of Industry 5.0 principles make the balance of human–robot collaborative disassembly lines an important research topic. This study uses disassembly-precedence graphs to clarify disassembly-task information and converts it into a task-precedence matrix. This matrix includes both symmetry and asymmetry, reflecting the dependencies and independencies among disassembly tasks. Based on this, we develop a multiobjective optimisation model that integrates disassembly-task allocation, operation mode selection, and the use of collaborative robots. The objectives are to minimise the number of workstations, the idle rate of the disassembly line, and the energy consumption. Given the asymmetry in disassembly-task attributes, such as the time differences required for disassembling various components and the diverse operation modes, this study employs an evolutionary algorithm to address potential asymmetric optimisation problems. Specifically, we introduce an advanced multi-objective discrete bee algorithm and validate its effectiveness and superiority for solving the disassembly-line balancing problem through a comparative analysis with other algorithms. This research not only provides innovative optimisation strategies for the product-recycling field but also offers valuable experience and reference for the further development of industrial automation and human–robot collaboration.

show abstract

“…However, it also has problems such as oscillation, insufcient convergence, and easily falling into local optimum. To improve the CSA's requirements for coal and gas outburst identifcation [10], speed up its convergence speed, improve its global search ability, eliminate its late-stage oscillation, and improve the success of identifcation rate, and then efectively identify prominent anomalies. Many scholars have done in-depth research on optimization selection, including the selfoptimization of immune algorithms, the use of distribution estimation algorithms, ant colony algorithms, and optimization of particle swarm optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on Risk Identification of Coal and Gas Outburst Based on PSO‐CSA

Peng

Shi

et al. 2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Aiming at the identification of coal and gas outburst risk, using the advantages of the clone selection algorithm (CSA), such as self-adaptation and robustness, and the characteristics of fast convergence of particle swarm optimization (PSO) algorithm, the complex decoding problem, and mutation process brought by CSA binary coding are used. It is difficult to control the problem. Using PSO optimization, the problem of abnormal detection and identification in coal and gas outburst monitoring is developed and studied, and a CSA coal and gas outburst risk anomaly detection and identification model based on PSO optimization variation is established. The model uses the coal and gas outburst index data as a collection of antigen-stimulated antibodies to achieve abnormal detection and identification of measured data. With the help of the measured data, the verification results show that the model can effectively detect and identify the risk of coal and gas outburst, and the identification results are consistent with the risk of coal and gas outburst in the field. It can be used as an effective risk identification model to guide coal mining work.

show abstract

A novel hybrid clonal selection algorithm for the corridor allocation problem with irregular material handling positions

Cited by 7 publications

References 39 publications

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

A Dual-Feedback Adaptive Clone Selection Algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

Human–Robot Collaboration on a Disassembly-Line Balancing Problem with an Advanced Multiobjective Discrete Bees Algorithm

Research on Risk Identification of Coal and Gas Outburst Based on PSO‐CSA

Contact Info

Product

Resources

About