Cai Xinyu, Zhang Chaoyong
College of Business, Jiaxing University, Jiaxing 314001, China.
School of Civil Engineering and Architecture, Jiaxing Nanhu University, Jiaxing 314000, China.
Biomimetics (Basel). 2025 Apr 23;10(5):260. doi: 10.3390/biomimetics10050260.
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.
在实际应用中，许多复杂问题都可以被表述为数学优化挑战，高效解决这些问题至关重要。元启发式算法已被证明在解决各种工程问题方面非常有效。差异化创造性搜索是最近提出的一种基于进化的元启发式算法，具有一定优势。然而，它也存在局限性，包括群体多样性减弱、搜索效率降低以及对解空间全面探索的阻碍。为了解决DCS算法的缺点，本文提出了一种基于协作发展机制和群体评估策略的多策略差异化创造性搜索（MSDCS）。首先，本文提出了一种协作发展机制，将估计分布算法和DCS有机结合，通过优势群体的引导作用弥补DCS算法探索能力不足和易陷入局部最优的缺点，同时提高DCS算法的搜索效率和解的质量。其次，提出了一种新的群体评估策略，通过对适应度和距离的综合评估实现利用和探索之间的协调过渡。最后，将线性种群规模缩减策略纳入DCS，通过在初始阶段保持较大种群规模以增强对解空间的探索能力和广泛搜索，然后在后期逐渐减小种群规模以增强利用能力，显著提高了算法的整体性能。在CEC2018测试集上进行了一系列验证，并使用Friedman检验和Wilcoxon秩和检验对实验结果进行了分析。结果表明，MSDCS在收敛速度、稳定性和全局优化方面具有卓越性能。此外，MSDCS成功应用于几个工程约束优化问题。在所有情况下，MSDCS都以快速收敛和强大的鲁棒性优于基本DCS算法，强调了其在实际应用中的卓越功效。
