Multiobjective evolutionary algorithms: analyzing the state-of-the-art

Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety, of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work.     

Complexity of cyclic scheduling problems: A state-of-the-art survey

In this survey we review the current complexity status of basic cyclic scheduling models. We start with the formulations of three fundamental cyclic scheduling problems, namely the cyclic jobshop, cyclic flowshop, and cyclic project scheduling problems. We present state-of-the-art results on the computational complexity of the problems, paying special attention to recent results on the unsolvability (NP-hardness) of various cyclic problems arising from the scheduling of robotic cells.     

Multiobjective scheduling algorithm for flexible manufacturing systems with Petri nets

In this work, we focus on general multi-objective scheduling problems that can be modeled using a Petri net framework. Due to their generality, Petri nets are a useful abstraction that captures multiple characteristics of real-life processes.To provide a general solution procedure for the abstraction, we propose three alternative approaches using an indirect scheme to represent the solution: (1) a genetic algorithm that combines two objectives through a weighted fitness function, (2) a non dominated sorting genetic algorithm (NSGA-II) that explicitly addresses the multi-objective nature of the problem and (3) a multi-objective local search approach that simultaneously explores multiple candidate solutions. These algorithms are tested in an extensive computational experiment showing the applicability of this general framework to obtain quality solutions.     

一种求解双目标job shop问题的混合进化算法

提出一种求解双目标job shop排序问题的混合进化算法．该算法采用改进的精英复制策略,降低了计算复杂性;通过引入递进进化模式,避免了算法的早熟;通过递进过程中的非劣解邻域搜索,增强了算法局部搜索性能．采用该算法和代表性算法NSGA-Ⅱ,MOGLS对82个标准双目标job shop算例进行优化对比,所得结果验证了该算法求解双目标job shop排序问题的有效性．     

Agent-based distributed manufacturing control: A state-of-the-art survey

Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re-usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future.     

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations.     

Network modeling and evolutionary optimization for scheduling in manufacturing

Scheduling is one of the most important fields in Advanced Planning and Scheduling or a manufacturing optimization. In this paper, we propose a network modeling technique to formulate the complex scheduling problems in manufacturing, and focus on how to model the scheduling problems to mathematical formulation. We propose a multi-section evolutionary algorithm for the scheduling models formulated by network modeling. Through a combination of the network modeling and this multi-section evolutionary algorithm, we can implement the auto-scheduling in the manufacturing system. The effectiveness and efficiency of proposed approach are investigated with various scales of scheduling problems by comparing with recent related researches. Lastly, we introduced service-oriented evolutionary computation architecture software. It help improved the evolutionary computation??s availability in the variable practical scheduling in manufacturing.     

求解多目标job-shop生产调度问题的量子进化算法*

基于量子计算理论和进化理论,提出了用于多目标job-shop优化的量子进化算法（QEA-MOJSP）。QEA-MOJSP采用量子比特来表示工序对加工顺序的优先概率,利用量子叠加和相干机理,通过更新和交叉操作完成进化过程。对所有机器上工序对优先概率进行观测可得到一个调度方案,修补算子被用于不可行调度方案的修补。设计了局部搜索算子用于开采当代最优个体周围的解空间,以提高算法的收敛速度。实验结果表明,对于测试算例,QEA-MOJSP的解接近Pareto最优解集前沿,并具有很好的多样性。     

Knowledge-based approaches for scheduling problems: a survey

Recent developments in artificial intelligence (AI) have led to the use of knowledge-based techniques for solving scheduling problems. The authors survey several existing intelligent planning and scheduling systems with the aim of providing a guide to the main AI techniques used. In view of the prevailing difference is usage of the terms planning and scheduling between AI and operations research (OR), a taxonomy of planning and scheduling problems is presented. The modeling of real world problems from closed deterministic worlds to complex real worlds is illustrated with a project scheduling example. Some of the more successful planning and scheduling systems are surveyed, and their features are highlighted. The AI approaches are consolidated into knowledge representation and problem solving in the project management context     

Expert systems in production planning and scheduling: A state-of-the-art survey

Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Especially the last decade has witnessed a growing number of manufacturing companies, including glass, oil, aerospace, computers, electronics, metal and chemical industries—to name just a few—interested in the applications of expert systems (ESs) in manufacturing. This paper is a state-of-the-art review of the use of ESs in the field of production planning and scheduling. The paper presents famous expert systems known in the literature and current applications, analyzes the relative benefits and concludes by sharing thoughts and estimations on ESs future prospects in this area.     

A block-based evolutionary algorithm for flow-shop scheduling problem

Combinatorial problems like flow shop scheduling, travel salesman problem etc. get complicated and are difficult to solve when the problem size increases. To overcome this problem, we present a block-based evolutionary algorithm (BBEA) which will conduct evolutionary operations on a set of blocks instead of genes. BBEA includes the block mining and block recombination approaches. A block mining algorithm is developed to decompose a chromosome into a set of blocks and rest of genes. The block is with a fixed length and can be treated as a building block in forming a new chromosome later on. To guide the block mining process, a gene linkage probability matrix is defined that shows the linkage strength among genes. Therefore the blocks can be further evolved during the evolutionary processes using this matrix. In the block recombination approach, the blocks along with the rest of genes are recombined to form a new chromosome. This new evolutionary approach of BBEA is tested on a set of discrete problems. Experimental results show that BBEA is very competitive when compared with traditional GA, EA or ACGA and HGIA approaches and it can largely improve the performance of evolutionary algorithm and save a fair amount of computational times simultaneously.     

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

The increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.     

Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm

There are various scheduling problems with resource limitations and constraints in the literature that can be modeled as variations of the Resource Constrained Project Scheduling Problem (RCPSP). This paper proposes a new solution representation and an evolutionary algorithm for solving the RCPSP. The representation scheme is based on an ordered list of events, that are sets of activities that start (or finish) at the same time. The proposed solution methodology, namely SAILS, operates on the event list and relies on a scatter search framework. The latter incorporates an Adaptive Iterated Local Search (AILS), as an improvement method, and integrates an event-list based solution combination method. AILS utilizes new enriched neighborhoods, guides the search via a long term memory and applies an efficient perturbation strategy. Computational results on benchmark instances of the literature indicate that both AILS and SAILS produce consistently high quality solutions, while the best results are derived for most problem data sets.     

解决多目标优化问题的差分进化算法研究进展

差分进化(differential evolution,DE)是一种简单但功能强大的进化优化算法.由于其优秀的性能,其诞生之日起就吸引了各国研究人员的关注.作为一种基于群体的全局性启发式搜索算法,差分进化算法在科学和工程中有许多成功的应用.本文对解决多目标优化问题的差分进化算法研究进行了综述,对差分进化的基本概念进行了详细的描述,给出了几种解决多目标优化问题的差分进化算法变体,并且给出了差分进化算法解决多目标优化问题的理论分析,最后,给出了差分进化算法解决多目标优化问题的工程应用,并指出了未来具有挑战性的研究领域.     

Hybrid evolutionary computation methods for quay crane scheduling problems

Quay crane scheduling is one of the most important operations in seaport terminals. The effectiveness of this operation can directly influence the overall performance as well as the competitive advantages of the terminal. This paper develops a new priority-based schedule construction procedure to generate quay crane schedules. From this procedure, two new hybrid evolutionary computation methods based on genetic algorithm (GA) and genetic programming (GP) are developed. The key difference between the two methods is their representations which decide how priorities of tasks are determined. While GA employs a permutation representation to decide the priorities of tasks, GP represents its individuals as a priority function which is used to calculate the priorities of tasks. A local search heuristic is also proposed to improve the quality of solutions obtained by GA and GP. The proposed hybrid evolutionary computation methods are tested on a large set of benchmark instances and the computational results show that they are competitive and efficient as compared to the existing methods. Many new best known solutions for the benchmark instances are discovered by using these methods. In addition, the proposed methods also show their flexibility when applied to generate robust solutions for quay crane scheduling problems under uncertainty. The results show that the obtained robust solutions are better than those obtained from the deterministic inputs.     

新颖的阻塞流水车间调度量子差分进化算法

针对阻塞流水车间调度问题(BFSP),提出了一种新颖的量子差分进化(NQDE)算法,用于最小化最大完工时间。该算法将量子进化算法(QEA)与差分进化(DE)相结合,设计一种新颖的量子旋转机制控制种群进化方向,增强种群多样性;采用高效的基于变邻域搜索的量子进化算法(QEA-VNS)协同进化策略增强算法的全局搜索能力,进一步提高解的质量。基于Taillard's benchmark实例仿真,结果表明,所提算法在最优解数量上明显高于目前较好的启发式算法--INEH,改进了110个实例中64个实例的当前最优解;在性能上也优于目前有效的元启发式算法--新型蛙跳算法(NMSFLA)和混合量子差分进化(HQDE),产生最优解的平均百分比偏差(ARPD)均下降约6%。NQDE算法适合大规模阻塞流水车间调度问题。     

Quantum inspired evolutionary algorithm for ordering problems

This paper proposes a new quantum-inspired evolutionary algorithm for solving ordering problems. Quantum-inspired evolutionary algorithms based on binary and real representations have been previously developed to solve combinatorial and numerical optimization problems, providing better results than classical genetic algorithms with less computational effort. However, for ordering problems, order-based genetic algorithms are more suitable than those with binary and real representations. This is because specialized crossover and mutation processes are employed to always generate feasible solutions. Therefore, this work proposes a new quantum-inspired evolutionary algorithm especially devised for ordering problems (QIEA-O). Two versions of the algorithm have been proposed. The so-called pure version generates solutions by using the proposed procedure alone. The hybrid approach, on the other hand, combines the pure version with a traditional order-based genetic algorithm. The proposed quantum-inspired order-based evolutionary algorithms have been evaluated for two well-known benchmark applications – the traveling salesman problem (TSP) and the vehicle routing problem (VRP) – as well as in a real problem of line scheduling. Numerical results were obtained for ten cases (7 VRP and 3 TSP) with sizes ranging from 33 to 101 stops and 1 to 10 vehicles, where the proposed quantum-inspired order-based genetic algorithm has outperformed a traditional order-based genetic algorithm in most experiments.     

柔性作业调度的串并行模型对比与求解

作业处理中的柔性使得作业调度更为灵活,作业中操作的执行顺序满足拓扑排序是作业调度的前提。是否允许没有优先关系的操作在不同的机器上同时执行是区分串行和并行调度的条件。文中以共生进化算法求解一个复杂的作业调度模型为例,给出了算法实现串行调度和并行调度的具体区别,并给出了串行和并行调度的结果。结果表明,并行相对于串行对算法效率的提高与柔性大小相关,与作业的规模成反比。