A novel multi-objective immune algorithm with a decomposition-based clonal selection

In recent years, a number of multi-objective immune algorithms (MOIAs) have been proposed as inspired by the information processing in biologic immune system. Since most MOIAs encourage to search around some boundary and less-crowded areas using the clonal selection principle, they have been validated to show the effectiveness on tackling various kinds of multi-objective optimization problems (MOPs). The crowding distance metric is often used in MOIAs as a diversity metric to reflect the status of population’s diversity, which is employed to clone less-crowded individuals for evolution. However, this kind of cloning may encounter some difficulties when tackling some complicated MOPs (e.g., the UF problems with variable linkages). To alleviate the above difficulties, a novel MOIA with a decomposition-based clonal selection strategy (MOIA-DCSS) is proposed in this paper. Each individual is associated to one subproblem using the decomposition approach and then the performance enhancement on each subproblem can be easily quantified. Then, a novel decomposition-based clonal selection strategy is designed to clone the solutions with the larger improvements for the subproblems, which encourages to search around these subproblems. Moreover, differential evolution is employed in MOIA-DCSS to strength the exploration ability and also to improve the population’s diversity. To evaluate the performance of MOIA-DCSS, twenty-eight test problems are used with the complicated Pareto-optimal sets and fronts. The experimental results validate the superiority of MOIA-DCSS over four state-of-the-art multi-objective algorithms (i.e., NSLS, MOEA/D-M2M, MOEA/D-DRA and MOEA/DD) and three competitive MOIAs (i.e., NNIA, HEIA, and AIMA).     

A double-module immune algorithm for multi-objective optimization problems

Multi-objective optimization problems (MOPs) have become a research hotspot, as they are commonly encountered in scientific and engineering applications. When solving some complex MOPs, it is quite difficult to locate the entire Pareto-optimal front. To better settle this problem, a novel double-module immune algorithm named DMMO is presented, where two evolutionary modules are embedded to simultaneously improve the convergence speed and population diversity. The first module is designed to optimize each objective independently by using a sub-population composed with the competitive individuals in this objective. Differential evolution crossover is performed here to enhance the corresponding objective. The second one follows the traditional procedures of immune algorithm, where proportional cloning, recombination and hyper-mutation operators are operated to concurrently strengthen the multiple objectives. The performance of DMMO is validated by 16 benchmark problems, and further compared with several multi-objective algorithms, such as NSGA-II, SPEA2, SMSEMOA, MOEA/D, SMPSO, NNIA and MIMO. Experimental studies indicate that DMMO performs better than the compared targets on most of test problems and the advantages of double modules in DMMO are also analyzed.     

An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems

This paper proposes an effective hybrid tabu search algorithm (HTSA) to solve the flexible job-shop scheduling problem. Three minimization objectives – the maximum completion time (makespan), the total workload of machines and the workload of the critical machine are considered simultaneously. In this study, a tabu search (TS) algorithm with an effective neighborhood structure combining two adaptive rules is developed, which constructs improved local search in the machine assignment module. Then, a well-designed left-shift decoding function is defined to transform a solution to an active schedule. In addition, a variable neighborhood search (VNS) algorithm integrating three insert and swap neighborhood structures based on public critical block theory is presented to perform local search in the operation scheduling component. The proposed HTSA is tested on sets of the well-known benchmark instances. The statistical analysis of performance comparisons shows that the proposed HTSA is superior to four existing algorithms including the AL + CGA algorithm by Kacem, Hammadi, and Borne (2002b), the PSO + SA algorithm by Xia and Wu (2005), the PSO + TS algorithm by Zhang, Shao, Li, and Gao (2009), and the Xing’s algorithm by Xing, Chen, and Yang (2009a) in terms of both solution quality and efficiency.     

求解模糊柔性Job-shop调度问题的多智能体免疫算法

考虑实际纸盆车间调度中模具、机器、操作人员等资源约束,以及加工时间和交货日期的不确定性,建立了批量可变的模糊柔性Job-shop调度问题模型.结合多智能体系统以及免疫信息处理机制,构造了一种求解实际Job-shop调度问题的多智能体免疫算法.该方法通过竞争、自学习、自适应疫苗接种、模拟退火等操作,更新每个智能体在解空间的位置,从而能精确地收敛到全局最优解.纸盆车间调度实例的求解结果验证了该算法的有效性.     

A novel hybrid multi-objective immune algorithm with adaptive differential evolution

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive differential evolution, named ADE-MOIA, in which the introduction of differential evolution (DE) into multi-objective immune algorithm (MOIA) combines their respective advantages and thus enhances the robustness to solve various kinds of MOPs. In ADE-MOIA, in order to effectively cooperate DE with MOIA, we present a novel adaptive DE operator, which includes a suitable parent selection strategy and a novel adaptive parameter control approach. When performing DE operation, two parents are respectively picked from the current evolved and dominated population in order to provide a correct evolutionary direction. Moreover, based on the evolutionary progress and the success rate of offspring, the crossover rate and scaling factor in DE operator are adaptively varied for each individual. The proposed adaptive DE operator is able to improve both of the convergence speed and population diversity, which are validated by the experimental studies. When comparing ADE-MOIA with several nature-inspired heuristic algorithms, such as NSGA-II, SPEA2, AbYSS, MOEA/D-DE, MIMO and D²MOPSO, simulations show that ADE-MOIA performs better on most of 21 well-known benchmark problems.     

A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem

In recent years, the historical data during the search process of evolutionary algorithms has received increasing attention from many researchers, and some hybrid evolutionary algorithms with machine-learning have been proposed. However, the majority of the literature is centered on continuous problems with a single optimization objective. There are still a lot of problems to be handled for multi-objective combinatorial optimization problems. Therefore, this paper proposes a machine-learning based multi-objective memetic algorithm (ML-MOMA) for the discrete permutation flowshop scheduling problem. There are two main features in the proposed ML-MOMA. First, each solution is assigned with an individual archive to store the non-dominated solutions found by it and based on these individual archives a new population update method is presented. Second, an adaptive multi-objective local search is developed, in which the analysis of historical data accumulated during the search process is used to adaptively determine which non-dominated solutions should be selected for local search and how the local search should be applied. Computational results based on benchmark problems show that the cooperation of the above two features can help to achieve a balance between evolutionary global search and local search. In addition, many of the best known Pareto fronts for these benchmark problems in the literature can be improved by the proposed ML-MOMA.     

A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean tardiness

This paper investigates a novel multi-objective model for a no-wait flow shop scheduling problem that minimizes both the weighted mean completion time and weighted mean tardiness . Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new hybrid multi-objective algorithm based on the features of a biological immune system (IS) and bacterial optimization (BO) to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective immune algorithm (HMOIA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various metrics, is compared against five prominent multi-objective evolutionary algorithms: PS-NC GA, NSGA-II, SPEA-II, MOIA, and MISA. Our computational results suggest that our proposed HMOIA outperforms the five foregoing algorithms, especially for large-sized problems.     

An efficient search method for multi-objective flexible job shop scheduling problems

Flexible job shop scheduling is very important in both fields of production management and combinatorial optimization. Owing to the high computational complexity, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches. Motivated by some empirical knowledge, we propose an efficient search method for the multi-objective flexible job shop scheduling problems in this paper. Through the work presented in this work, we hope to move a step closer to the ultimate vision of an automated system for generating optimal or near-optimal production schedules. The final experimental results have shown that the proposed algorithm is a feasible and effective approach for the multi-objective flexible job shop scheduling problems.     

A high performing metaheuristic for multi-objective flowshop scheduling problem

Genetic algorithm is a powerful procedure for finding an optimal or near optimal solution for the flowshop scheduling problem. This is a simple and efficient algorithm which is used for both single and multi-objective problems. It can easily be utilized for real life applications. The proposed algorithm makes use of the principle of Pareto solutions. It mines the Pareto archive to extract the most repetitive sequences, and constitutes artificial chromosome for generation of the next population. In order to guide the search direction, this approach coupled with variable neighborhood search. This algorithm is applied on the flowshop scheduling problem for minimizing makespan and total weighted tardiness. For the assessment of the algorithm, its performance is compared with the MOGLS [1]. The results of the experiments allow us to claim that the proposed algorithm has a considerable performance in this problem.     

基于混合人工蜂群算法的多目标柔性作业车间调度问题研究

传统的优化算法在求解面对多目标柔性作业车间调度时,往往求解效率低且难以获得最优解。为了求解多目标柔性作业车间调度问题,设计了混合人工蜂群算法。种群的初始化采用了多种方法相结合的策略。在人工蜂群算法的不同阶段采用不同的搜索机制,在雇佣蜂阶段采用开发搜索,针对跟随蜂阶段蜜蜂跟随的对象的优秀解进行小幅度的更新,从而提高了搜索的表现。禁忌搜索与改进的人工蜂群算法相结合,有效的提升了获得最优解的概率。通过相关文献中的标准实例对设计的混合人工蜂群算法进行一系列求解测试,实验的结果有效的说明了算法在求解柔性作业车间调度问题时效果显著。通过求解结果对比表明人工蜂群算法的高效性和优越性。     

A multi-objective evolutionary approach for generator scheduling

This paper presents a novel, two-phase approach for optimal generation scheduling, taking into account the environmental issue of emission allowance trading in addition to the economic issue of operation cost. In the first phase, hourly-optimal scheduling is done to simultaneously minimize operation cost, emission, and transmission loss, while satisfying constraints such as power balance, spinning reserve and power generation limits. In the second phase, the minimum up/down time and ramp up/down rate constraints are considered, and a set of 24-h optimal schedules is obtained using the outputs of the first phase. Simulation results indicate effectiveness of the proposed approach.     

Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.     

具有模糊多目标的网格任务调度算法

在异构的网格计算平台上,网格中有用户、资源管理员、组织管理者等实体,这些实体对网格的管理、使用、维护、安全性、可靠性等目标都提出了要求,并且这些目标有时是不可量化的。针对具有模糊多目标网格计算的任务调度问题,提出模糊多目标网格任务调度模型,使用模糊化等式对多目标进行模糊处理,给出求解该模型的模糊化定理,并对该定理进行证明。利用差分优化算法无需目标函数连续可微的特点,提出使用模糊差分优化算法完成模糊多目标的网格任务调度。实验结果表明,模糊差分优化算法较现有算法在执行时间上处于劣势,但在可靠性、安全性和丢失任务数三个指标上要优于现有算法。     

云环境中基于分解的多目标工作流调度算法

云服务提供商在给用户提供海量虚拟资源的同时,也面临着一个现实的问题,即怎样调度这些资源,以最小的代价(完工时间、执行费用、资源利用率等)完成工作流的执行。针对IaaS环境下的工作流调度问题,以完工时间和执行费用作为目标,提出了一种基于分解的多目标工作流调度算法。该算法结合了基于列表的启发式算法和多目标进化算法的选择过程,采用一种分解方法,将多目标优化问题分解为一组单目标优化子问题,然后同时求解这些单目标子问题,使得调度过程更为简单有效。算法利用天马项目发布的现实世界中的工作流进行实验,结果表明,和MOHEFT算法以及NSGA-II*算法相比较,所提出的算法能得到更优的Pareto解集,同时具有更低的时间复杂度。     

基于TS/VDS算法的涤纶短纤维生产调度优化

采用禁忌搜索（ＴＳ）／变深度搜索（ＶＤＳ）混合算法对涤纶短纤维生产调度优化问题进行优化．混合算法通过改变常规ＴＳ算法邻域,采用变深度搜索技术增强了算法寻优能力．某大型石化企业实际数据的实验结果表明,该算法在寻优能力和求解时间上比常规ＴＳ算法更加有效,能够在更短的时间内获得满意解,对于解决多产品多阶段连续生产调度问题具有实用价值．     

A multi-objective co-evolutionary algorithm for energy-efficient scheduling on a green data center

Nowadays, the environment protection and the energy crisis prompt more computing centers and data centers to use the green renewable energy in their power supply. To improve the efficiency of the renewable energy utilization and the task implementation, the computational tasks of data center should match the renewable energy supply. This paper considers a multi-objective energy-efficient task scheduling problem on a green data center partially powered by the renewable energy, where the computing nodes of the data center are DVFS-enabled. An enhanced multi-objective co-evolutionary algorithm, called OL-PICEA-g, is proposed for solving the problem, where the PICEA-g algorithm with the generalized opposition based learning is applied to search the suitable computing node, supply voltage and clock frequency for the task computation, and the smart time scheduling strategy is employed to determine the start and finish time of the task on the chosen node. In the experiments, the proposed OL-PICEA-g algorithm is compared with the PICEA-g algorithm, the smart time scheduling strategy is compared with two other scheduling strategies, i.e., Green-Oriented Scheduling Strategy and Time-Oriented Scheduling Strategy, different parameters are also tested on the randomly generated instances. Experimental results confirm the superiority and effectiveness of the proposed algorithm.     

A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly

Some manufacturers outsource their disassembly tasks to professional factories, each factory of them has specialized in its disassembly ability. Different disassembly facilities are usually combined to execute disassembly tasks. This study proposes the cloud-based disassembly that abstracts ability of the disassembly factory as the disassembly resource, the disassembly resource is then able to be allocated to execute disassembly tasks. Based on this concept, the cloud-based disassembly system is proposed, which provides the disassembly service according to the user requirement. The disassembly service is the execution plan for disassembly tasks, which is the result of scheduling disassembly tasks and allocating disassembly resources. To formally describe the disassembly service, this paper builds a mathematical model that considers the uncertainty nature of the disassembly process and precedence relationships of disassembly tasks. Two objectives including minimizing the expected total makespan and minimizing the expected total cost of the disassembly service are also discussed. The mathematical model is NP-complete, a multi-objective genetic algorithm based on non-dominated sorting genetic algorithm II is designed to address the problem. Computation results show that the proposed algorithm performs well, the algorithm generates a set of Pareto optimal solutions. The user can choose a preferred disassembly service among Pareto optimal solutions.     

一类复杂生产调度问题的基于符号演绎的调度方法

针对带有时问不确定件的复杂生产过程调度问题,提出一种基于符号演绎的调度方法.首先将时间的不确定性信息看作符号型数据,并提出一种用于处理这些符号型数据的基于不确定区间的符号演绎方法;然后将此符号演绎方法与遗传算法相结合,提出一种预排调度计划与实时调度规则相结合的调度方法来求解上述复杂生产调度问题.实验表明,将基于符号演绎的调度方法用于求解带有时间不确定性的复杂生产过程调度问题,能够取得较好的调度效果.     

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.