PDF(Pubmed)
Abstract：
This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.
摘要：
本研究引入了多目标肝癌算法(MOLCA),一种受肝脏肿瘤生长和增殖模式启发的新方法。MOLCA模仿肝脏肿瘤的进化趋势，利用它们的扩展动力学作为解决工程设计中的多目标优化问题的模型。该算法独特地将遗传算子与随机基于对立的学习（ROBL）策略相结合，优化本地和全局搜索功能。通过整合精英非主导排序(NDS)，进一步增强信息反馈机制(IFM)和拥挤距离(CD)选择方法，它们的共同目标是有效地识别帕累托最优前沿。MOLCA的性能使用一套全面的标准多目标测试基准进行严格评估，包括ZDT,DTLZ和各种约束(CONSTR，TNK,SRN,BNH,OSY和KITA）和实际工程设计问题，例如无刷直流轮毂电机，安全隔离变压器,螺旋弹簧,双杆桁架和焊接梁。它的功效以突出的算法为基准，例如非主导排序灰狼优化器（NSGWO），多目标多逆优化(MOMVO)，非支配排序遗传算法(NSGA-II)，基于分解的多目标进化算法(MOEA/D)和多目标海洋捕食者算法(MOMPA)。使用GD进行定量分析，IGD,SP,SD,表示收敛和分布的HV和RT指标，而定性方面是通过帕累托战线的图形表示来呈现的。MOLCA源代码可在以下网址获得：https://github.com/kanak02/MOLCA。
