多目标拆卸线平衡问题的Pareto细菌觅食算法*

拆卸线平衡问题的优化涉及多个目标,为克服传统方法在求解多目标拆卸线平衡问题时不能很好处理各子目标间冲突及易于早熟等不足,提出了一种多目标细菌觅食优化算法。算法采用Pareto非劣排序技术对种群进行分级,并结合拥挤距离机制评价同级个体的优劣。为提高算法收敛性能,在趋向性操作结束后引入精英保留策略保留优秀个体,并采用全局信息共享策略引导菌群不断向均匀分布的Pareto最优前沿趋近。通过不同规模算例的对比验证表明了算法的有效性与优越性。     

考虑零件分类的U型拆卸线平衡问题与改进烟花算法求解

针对拆卸生产线中存在的不确定性和零件复杂性,构建以最小化工作站数、空闲指标、拆卸成本及零件分类指标的多目标数学模型并采用一种改进烟花算法对所提模型进行求解.首先,考虑所求解问题的特性对烟花算法进行离散化处理,重新定义了爆炸操作和变异操作,烟花个体产生爆炸火花和变异火花之后引入Pareto解集思想和NSGA-II拥挤距离机制对可行解进行筛选并更新烟花个体.其次,将所提烟花算法分别应用于求解中规模直线型和大规模U型拆卸线平衡问题算例中,并与其它算法的求解结果对比,验证改进烟花算法在直线型和U型拆卸线上的求解性能.最后,将所建模型和算法应用到打印机拆卸线中,并与直线型求解结果进行对比,对比结果表明所提方法有效可行.     

基于Pareto的多目标优化免疫算法

免疫算法具有搜索效率高、避免过早收敛、群体优化、保持个体多样性等优点。将其应用于多目标优化问题,建立了一种新型的基于Pareto的多目标优化免疫算法(MOIA)。算法中,将优化问题的可行解对应抗体,优化问题的目标函数对应抗原,Pareto最优解被保存在记忆细胞集中,并利用有别于聚类的邻近排挤算法对其进行不断更新,进而获得分布均匀的Pareto最优解。文章最后,对MOIA算法与文献[3]中SPEA算法进行仿真,通过比较两者的收敛性和分布性,得到了MOIA优于SPEA的结论。     

Pareto强度值演化算法求解多目标优化问题

近年来,多目标优化问题求解已成为演化计算的一个重要研究方向,而基于Pareto最优概念的多目标演化算法则是当前演化计算的研究热点.多目标演化算法的研究目标是使算法种群快速收敛并均匀分布于问题的非劣最优域.本文定义和使用稀松密度来保持群体中个体的均匀分布,并将个体的Pareto强度值和稀松密度合并到个体的适应值定义中.通过对测试函数的实验,验证了算法的可行性和有效性.     

基于模拟退火的多目标优化算法

该文剖析了多目标优化问题和物体退火之间的关系,发现两者之间有着天然的联系,并在此联系的基础上,构建了一种新型的多目标优化算法———基于模拟退火的多目标优化算法。最后,基于典型算例的数值仿真验证了算法的有效性。     

多目标微粒群优化算法

通过设计一种Pareto解集过滤器,并在此基础上给出多目标优化条件下的微粒群算法群体停滞判断准则,基于该准则提出了一种多目标微粒群优化算法。算法利用Pareto解集过滤器提高了候选解的多样性,并使用图形法将所提算法与经典的多目标优化进化算法在一组标准测试函数上进行了比较,结果表明算法具有更好的搜索效率。     

多目标优化的一类模拟退火算法

多目标优化是运筹学中的重要研究课题,但迄今仍缺少高效的优化技术。通过对搜索操作和参数的合理设置,提出了一类求解多目标优化问题Pareto最优解的高效模拟退火算法。基于典型算例的数值仿真验证了算法的有效性。     

求解机械优化的Pareto多目标中心粒子群算法

针对基于权重法的多目标算法无法求解约束多目标问题的缺陷,将中心粒子群算法与Pareto解集搜索算法相结合,提出一种Pareto多目标中心粒子群算法。将此方法用来优化气门弹簧的模型,实验结果表明,该优化方法能够快速准确地收敛于Pareto解集,并且使其对应的目标域均匀地分布于Pareto最优目标域。     

拆卸线平衡问题的改进细菌觅食优化算法

拆卸是产品回收过程中最重要的环节,拆卸过程高效与否直接影响产品的回收效率。为克服传统算法求解拆卸线平衡问题时性能不稳定的缺陷,在构建基于工作站利用率、负荷均衡,尽早拆卸有危害、高需求的零件,最小化拆卸成本等方面的拆卸线平衡问题多目标优化模型的基础上,提出一种改进的细菌觅食优化算法对问题求解。通过改进细菌的移动规则扩大搜索空间,引入全局信息共享策略增强算法收敛性能,定义了一种自适应驱散概率防止驱散操作中解的退化。在对不同规模算例的对比分析中,验证了该算法的有效性。     

带硬时间窗模糊车辆路径问题的多目标优化

针对带硬时间窗车辆路径问题的多重模糊性,基于模糊可信性理论建立多目标模糊期望值模型,提出求解该问题的自适应混合多目标粒子群优化算法.该算法根据相位空间的思想给出一种实数编码方式,设计双存档机制,分别存储演化过程中产生的非支配解和有益不可行解,并引入自适应局部搜索、变异和粒子全局向导选择策略.仿真实验结果表明,与多目标进化算法相比,该算法可以获得更优的Pareto解集.     

Evolutionary optimization using epsilon method for resource-constrained multi-robotic disassembly line balancing

This paper concentrates on a resource-constrained multi-robotic disassembly line balancing (RC-MDLB) problem. In this RC-MDLB problem, different types of end-of-life products are disassembled simultaneously on the same line under the following conditions: allocating multiple robots to a workstation to simultaneously process the disassembly tasks that have no precedence relationship with each other, each robot needs a fixed number of limited resources to process tasks, and the total resources for each workstation is fixed. A mathematical model is presented for the RC-MDLB problem to minimize the cycle time and the number of robots being occupied simultaneously. A constrained multi-objective evolutionary algorithm framework and a constrained NSGA-II (E-NSGA-II) algorithm with epsilon method are proposed to handle the constraints of the RC-MDLB problem. The proposed E-NSGA-II is applied to a set of RC-MDLB problem instances introduced in this paper and compared with five representative multi-objective evolutionary algorithms. The experimental results reveal that the proposed E-NSGA-II presents outstanding performance on most of the cases analyzed.     

Multi-objective optimization of stochastic disassembly line balancing with station paralleling

One of the major activities performed in product recovery is disassembly. Disassembly line is the most suitable setting to disassemble a product. Therefore, designing and balancing efficient disassembly systems are important to optimize the product recovery process. In this study, we deal with multi-objective optimization of a stochastic disassembly line balancing problem (DLBP) with station paralleling and propose a new genetic algorithm (GA) for solving this multi-objective optimization problem. The line balance and design costs objectives are simultaneously optimized by using an AND/OR Graph (AOG) of the product. The proposed GA is designed to generate Pareto-optimal solutions considering two different fitness evaluation approaches, repair algorithms and a diversification strategy. It is tested on 96 test problems that were generated using the benchmark problem generation scheme for problems defined on AOG as developed in literature. In addition, to validate the performance of the algorithm, a goal programming approach and a heuristic approach are presented and their results are compared with those obtained by using GA. Computational results show that GA can be considered as an effective and efficient solution algorithm for solving stochastic DLBP with station paralleling in terms of the solution quality and CPU time.     

基于Pareto邻域交叉算子的多目标粒子群优化算法

针对粒子群优化（PSO）算法局部搜索能力不足的问题,提出一种基于Pareto邻域交叉算子的多目标粒子群优化算法(MPSOP)。该算法利用粒子群优化算法和Pareto邻域交叉算子相结合的策略产生新种群,并利用尺度因子在线调节粒子群优化算法和Pareto邻域交叉算子的贡献量。数值实验选取6个常用测试函数并对NSGA-Ⅱ、SPEA2、MOPSO三个多目标算法进行比较,数值实验结果表明MPSOP算法的有效性。     

Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem

In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures. Since the complexity of SDDLBP increases with the number of parts of the product, an efficient methodology based on artificial bee colony (ABC) is proposed to solve the SDDLBP. ABC is an optimization technique which is inspired by the behavior of honey bees. The performance of the proposed algorithm was tested against six other algorithms. The results show that the proposed ABC algorithm performs well and is superior to the other six algorithms in terms of the objective values performance.     

A Pareto-discrete hummingbird algorithm for partial sequence-dependent disassembly line balancing problem considering tool requirements

To reduce the disassembly costs to enterprises and improve the disassembly efficiency of waste products, this study proposed a partial sequence-dependent disassembly line balancing problem (PSD-DLBP) and established a multi-objective mathematical model to simultaneously minimize the number of workstations, total disassembly time, idle balance index and the number of disassembly tools. Then, a Pareto-discrete hummingbird algorithm (PDHA) was proposed to address PSD-DLBP effectively. The PDHA includes two stages: self-searching stage and information-interacting stage. With these two stages, the exploration and exploitation abilities of PDHA can be balanced. Later, the effectiveness and superiority of the PDHA were verified by comparing it with the other four algorithms for two different-scale examples. Finally, the model and PDHA were applied to the optimization of a partial sequence-dependent disassembly line of waste laptops. The optimization results show that the partial disassembly can make the line smoother and the utilization efficiency of workstations higher than full disassembly, and PDHA is superior in solving the PSD-DLBP.     

多目标优化问题的研究概述*

详细介绍了实际生活中存在的多目标优化问题以及解决多目标优化问题的几种典型算法,讨论了各个算法存在的优缺点, 并且列举了近年来在各个领域中出现的多目标优化问题;最后对多目标优化算法的未来发展方向进行展望。     

混合分解和强度帕累托多目标进化算法

在多目标进化优化中,使用分解策略的基于分解的多目标进化算法(MOEA/D)时间复杂度低,使用〖BP(〗强度帕累托策略的〖BP)〗强度帕累托进化算法-2(SPEA2)能得到分布均匀的解集。结合这两种策略,提出一种新的多目标进化算法用于求解具有复杂、不连续的帕累托前沿的多目标优化问题(MOP)。首先,利用分解策略快速逼近帕累托前沿;然后,利用强度帕累托策略使解集均匀分布在帕累托前沿,利用解集重置分解策略中的权重向量集,使其适配于特定的帕累托前沿;最后,利用分解策略进一步逼近帕累托前沿。使用的反向世代距离(IGD)作为度量标准,将新算法与MOEA/D、SPEA2和paλ-MOEA/D在12个基准问题上进行性能对比。实验结果表明该算法性能在7个基准问题上最优,在5个基准问题上接近于最优,且无论MOP的帕累托前沿是简单或复杂、连续或不连续的,该算法均能生成分布均匀的解集。     

Assembly line balancing using genetic algorithms

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.