An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation
In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/10/5/260 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850127574037954560 |
|---|---|
| author | Xinyu Cai Chaoyong Zhang |
| author_facet | Xinyu Cai Chaoyong Zhang |
| author_sort | Xinyu Cai |
| collection | DOAJ |
| description | In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced search efficiency, and hindrance of comprehensive exploration of the solution space. To address the shortcomings of the DCS algorithm, this paper proposes a multi-strategy differentiated creative search (MSDCS) based on the collaborative development mechanism and population evaluation strategy. First, this paper proposes a collaborative development mechanism that organically integrates the estimation distribution algorithm and DCS to compensate for the shortcomings of the DCS algorithm’s insufficient exploration ability and its tendency to fall into local optimums through the guiding effect of dominant populations, and to improve the quality of the DCS algorithm’s search efficiency and solution at the same time. Secondly, a new population evaluation strategy is proposed to realize the coordinated transition between exploitation and exploration through the comprehensive evaluation of fitness and distance. Finally, a linear population size reduction strategy is incorporated into DCS, which significantly improves the overall performance of the algorithm by maintaining a large population size at the initial stage to enhance the exploration capability and extensive search of the solution space, and then gradually decreasing the population size at the later stage to enhance the exploitation capability. A series of validations was conducted on the CEC2018 test set, and the experimental results were analyzed using the Friedman test and Wilcoxon rank sum test. The results show the superior performance of MSDCS in terms of convergence speed, stability, and global optimization. In addition, MSDCS is successfully applied to several engineering constrained optimization problems. In all cases, MSDCS outperforms the basic DCS algorithm with fast convergence and strong robustness, emphasizing its superior efficacy in practical applications. |
| format | Article |
| id | doaj-art-e2d84c37fc2f4be8bae9e39b25b09e86 |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-e2d84c37fc2f4be8bae9e39b25b09e862025-08-20T02:33:38ZengMDPI AGBiomimetics2313-76732025-04-0110526010.3390/biomimetics10050260An Innovative Differentiated Creative Search Based on Collaborative Development and Population EvaluationXinyu Cai0Chaoyong Zhang1College of Business, Jiaxing University, Jiaxing 314001, ChinaSchool of Civil Engineering and Architecture, Jiaxing Nanhu University, Jiaxing 314000, ChinaIn real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced search efficiency, and hindrance of comprehensive exploration of the solution space. To address the shortcomings of the DCS algorithm, this paper proposes a multi-strategy differentiated creative search (MSDCS) based on the collaborative development mechanism and population evaluation strategy. First, this paper proposes a collaborative development mechanism that organically integrates the estimation distribution algorithm and DCS to compensate for the shortcomings of the DCS algorithm’s insufficient exploration ability and its tendency to fall into local optimums through the guiding effect of dominant populations, and to improve the quality of the DCS algorithm’s search efficiency and solution at the same time. Secondly, a new population evaluation strategy is proposed to realize the coordinated transition between exploitation and exploration through the comprehensive evaluation of fitness and distance. Finally, a linear population size reduction strategy is incorporated into DCS, which significantly improves the overall performance of the algorithm by maintaining a large population size at the initial stage to enhance the exploration capability and extensive search of the solution space, and then gradually decreasing the population size at the later stage to enhance the exploitation capability. A series of validations was conducted on the CEC2018 test set, and the experimental results were analyzed using the Friedman test and Wilcoxon rank sum test. The results show the superior performance of MSDCS in terms of convergence speed, stability, and global optimization. In addition, MSDCS is successfully applied to several engineering constrained optimization problems. In all cases, MSDCS outperforms the basic DCS algorithm with fast convergence and strong robustness, emphasizing its superior efficacy in practical applications.https://www.mdpi.com/2313-7673/10/5/260differentiated creative searchmetaheuristic algorithmengineering optimization problemscollaborative development mechanismlinear population size reduction |
| spellingShingle | Xinyu Cai Chaoyong Zhang An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation Biomimetics differentiated creative search metaheuristic algorithm engineering optimization problems collaborative development mechanism linear population size reduction |
| title | An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation |
| title_full | An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation |
| title_fullStr | An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation |
| title_full_unstemmed | An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation |
| title_short | An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation |
| title_sort | innovative differentiated creative search based on collaborative development and population evaluation |
| topic | differentiated creative search metaheuristic algorithm engineering optimization problems collaborative development mechanism linear population size reduction |
| url | https://www.mdpi.com/2313-7673/10/5/260 |
| work_keys_str_mv | AT xinyucai aninnovativedifferentiatedcreativesearchbasedoncollaborativedevelopmentandpopulationevaluation AT chaoyongzhang aninnovativedifferentiatedcreativesearchbasedoncollaborativedevelopmentandpopulationevaluation AT xinyucai innovativedifferentiatedcreativesearchbasedoncollaborativedevelopmentandpopulationevaluation AT chaoyongzhang innovativedifferentiatedcreativesearchbasedoncollaborativedevelopmentandpopulationevaluation |