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.     

基于混合粒子群算法的网格任务调度

减少分布式程序的执行时间是网格调度系统需要解决的重要问题。因分布式程序常建模为DAG图,故该问题又称异构DAG调度问题。在研究网格环境下的任务调度的基础上,提出了一种用于解决DAG任务调度问题的通用混合粒子群优化算法(Common Hybrid Particle Swarm Optimization),简称为CHPSO。该算法将问题的解(粒子)表示为任务的调度优先权向量,采用混合粒子群优化算法探索解空间。实验结果表明,在求解不含孤立点的单个DAG调度问题时,该算法所得解的调度长度仅为HEFT的90%～92%,求解质量与PSGA相当;在多张DAG图(含孤立节点)并发执行的网格环境中,该算法的调度性能明显优于PSGA及文中列出的其它演化计算方法。     

A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

A novel hybrid PSO–GWO approach for unit commitment problem

Particle swarm optimization algorithm is a inhabitant-based stochastic search procedure, which provides a populace-based search practice for getting the best solution from the problem by taking particles and moving them around in the search space and efficient for global search. Grey Wolf Optimizer is a recently developed meta-heuristic search algorithm inspired by Canis-lupus. This research paper presents solution to single-area unit commitment problem for 14-bus system, 30-bus system and 10-generating unit model using swarm-intelligence-based particle swarm optimization algorithm and a hybrid PSO–GWO algorithm. The effectiveness of proposed algorithms is compared with classical PSO, PSOLR, HPSO, hybrid PSOSQP, MPSO, IBPSO, LCA–PSO and various other evolutionary algorithms, and it is found that performance of NPSO is faster than classical PSO. However, generation cost of hybrid PSO–GWO is better than classical and novel PSO, but convergence of hybrid PSO–GWO is much slower than NPSO due to sequential computation of PSO and GWO.     

Branch-and-bound and PSO algorithms for no-wait job shop scheduling

This paper deals with the no-wait job shop scheduling problem resolution. The problem is to find a schedule to minimize the makespan (\(C_{max}\)), that is, the total completeness time of all jobs. The no-wait constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. For this, we have proposed two different resolution methods, the first is an exact method based on the branch-and-bound algorithm, in which we have defined a new technique of branching. The second is a particular swarm optimization (PSO) algorithm, extended from the discrete version of PSO. In the proposed algorithm, we have defined the particle and the velocity structures, and an efficient approach is developed to move a particle to the new position. Moreover, we have adapted the timetabling procedure to find a good solution while respecting the no-wait constraint. Using the PSO method, we have reached good results compared to those in the literature.     

A hybrid particle swarm optimization for job shop scheduling problem

A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed in this paper. In previous research, PSO particles search solutions in a continuous solution space. Since the solution space of the JSP is discrete, we modified the particle position representation, particle movement, and particle velocity to better suit PSO for the JSP. We modified the particle position based on preference list-based representation, particle movement based on swap operator, and particle velocity based on the tabu list concept in our algorithm. Giffler and Thompson’s heuristic is used to decode a particle position into a schedule. Furthermore, we applied tabu search to improve the solution quality. The computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.     

一种求解作业车间调度的混合粒子群算法*

针对车间作业调度问题,提出了一种混合了知识进化算法和粒子群优化的算法。算法主要是结合知识进化算法的进化选择机制和粒子群优化的局部快速收敛性特性,首先让粒子替代知识进化算法中的进化个体,在群体空间中按粒子群优化规则寻找局部最优,然后根据知识进化算法的全局选择机制寻找全局最优,最后,将车间作业调度问题的特点融入到所提出的混合算法中求解问题。采用基准数据进行测试的仿真实验,并比对标准遗传算法,结果表明所提算法的有效性。     

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.     

批量无等待调度问题的微粒群蛙跳混合优化算法*

将离散微粒群与蛙跳算法相结合解决以最大完工时间为指标的批量无等待流水线调度问题.结合微粒群算法较强的全局收敛能力和蛙跳算法较强的深度搜索能力,设计了三种混合算法,平衡了算法的全局开发能力和局部探索能力.对随机生成不同规模的实例进行了广泛的实验,仿真实验结果的比较表明了所得混合算法的有效性和高效性.     

A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem

Job shop scheduling problem (JSP) which is widespread in the real-world production system is one of the most general and important problems in various scheduling problems. Nowadays, the effective method for JSP is a hot topic in research area of manufacturing system. JSP is a typical NP-hard combinatorial optimization problem and has a broad engineering application background. Due to the large and complicated solution space and process constraints, JSP is very difficult to find an optimal solution within a reasonable time even for small instances. In this paper, a hybrid particle swarm optimization algorithm (PSO) based on variable neighborhood search (VNS) has been proposed to solve this problem. In order to overcome the blind selection of neighborhood structures during the hybrid algorithm design, a new neighborhood structure evaluation method based on logistic model has been developed to guide the neighborhood structures selection. This method is utilized to evaluate the performance of different neighborhood structures. Then the neighborhood structures which have good performance are selected as the main neighborhood structures in VNS. Finally, a set of benchmark instances have been conducted to evaluate the performance of proposed hybrid algorithm and the comparisons among some other state-of-art reported algorithms are also presented. The experimental results show that the proposed hybrid algorithm has achieved good improvement on the optimization of JSP, which also verifies the effectiveness and efficiency of the proposed neighborhood structure evaluation method.     

A hybrid particle swarm optimization based algorithm for high school timetabling problems

In this contribution a hybrid particle swarm optimization (PSO) based algorithm is applied to high school timetabling problems. The proposed PSO based algorithm is used for creating feasible and efficient high school timetables. In order to demonstrate the efficiency of the proposed PSO based algorithm, experiments with real-world input data coming from many different Greek high schools have been conducted. Computational results show that the proposed hybrid PSO based algorithm performs better than existing approaches applied to the same school timetabling input instances using the same evaluation criteria.     

基于GA-PSO的天基预警系统资源调度方法

为解决天基预警系统中的卫星资源调度问题,从预警任务特点出发,在对预警任务进行分解的基础上,建立了资源调度模型．结合传统遗传算法（GA）和粒子群算法（PSO）的优点,采用一种混合遗传粒子群（GA-PSO）算法来求解资源调度问题．该算法在解决粒子编解码问题的前提下,将遗传算法的遗传算子应用于粒子群算法,改善了粒子群算法的寻优能力．实验结果表明,提出的算法能有效解决多目标探测时天基预警系统的资源调度问题,调度结果优于传统粒子群算法和遗传算法．     

Solving a periodic single-track train timetabling problem by an efficient hybrid algorithm

Train timetabling with minimum delays is the most important operating problem in any railway industry. This problem is considered to be one of the most interesting research topics in railway optimization problems. This paper deals with scheduling different types of trains in a single railway track. The primary focus of this paper is on the periodic aspects of produced timetables and the proposed modeling is based on the periodic event scheduling problem (PESP). To solve large-scale problems, a hybrid meta-heuristic algorithm based on simulated annealing (SA) and particle swarm optimization (PSO) is proposed and validated using some numerical examples and an Iranian case study that covers the railway line between two cities of Isfahan and Tehran.     

A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing

Scheduling means devoting tasks among computational resources, considering specific goals. Cloud computing is facing a dynamic and rapidly evolving situation. Devoting tasks to the computational resources could be done in numerous different ways. As a consequence, scheduling of tasks in cloud computing is considered as a NP-hard problem. Meta-heuristic algorithms are a proper choice for improving scheduling in cloud computing, but they should, of course, be consistent with the dynamic situation in the field of cloud computing. One of the newest bio-inspired meta-heuristic algorithms is the chicken swarm optimization (CSO) algorithm. This algorithm is inspired by the hierarchical behavior of chickens in a swarm for finding food. The diverse movements of the chickens create a balance between the local and the global search for finding the optimal solution. Raven roosting optimization (RRO) algorithm is inspired by the social behavior of raven and the information flow between the members of the population with the goal of finding food. The advantage of this algorithm lies in using the individual perception mechanism in the process of searching the problem space. In the current work, an ICDSF scheduling framework is proposed. It is a hybrid (IRRO-CSO) meta-heuristic approach based on the improved raven roosting optimization algorithm (IRRO) and the CSO algorithm. The CSO algorithm is used for its efficiency in satisfying the balance between the local and the global search, and IRRO algorithm is chosen for solving the problem of premature convergence and its better performance in bigger search spaces. First, the performance of the proposed hybrid IRRO-CSO algorithm is compared with other imitation-based swarm intelligence methods using benchmark functions (CEC 2017). Then, the capabilities of the proposed scheduling hybrid algorithm (IRRO-CSO) are tested using the NASA-iPSC parallel workload and are compared with the other available algorithms. The obtained results from the implementation of the hybrid IRRO-CSO algorithm in MATLAB show an improvement in the average best fitness compared with the following algorithms: IRRO, RRO, CSO, BAT and PSO. Finally, simulation tests performed in cloud computing environment show improvements in terms of reduction of execution time, reduction of response time and the increase in throughput by using the proposed hybrid IRRO-CSO approach for dynamic scheduling.     

Local search techniques for large high school timetabling problems

The high school timetabling problem regards the weekly scheduling for all the lectures of a high school. The problem consists in assigning lectures to periods in such a way that no teacher (or class) is involved in more than one lecture at a time, and other constraints are satisfied. The problem is NP-complete and is usually tackled using heuristic methods, This paper describes a solution algorithm (and its implementation) based on local search techniques. The algorithm alternates different techniques and different types of moves and makes use of an adaptive relaxation of the hard constraints. The implementation of the algorithm has been successfully experimented with in some large high schools with various kinds of side constraints     

An AIS-based hybrid algorithm for static job shop scheduling problem

A static job shop scheduling problem (JSSP) is a class of JSSP which is a combinatorial optimization problem with the assumption of no disruptions and previously known knowledge about the jobs and machines. A new hybrid algorithm based on artificial immune systems (AIS) and particle swarm optimization (PSO) theory is proposed for this problem with the objective of makespan minimization. AIS is a metaheuristics inspired by the human immune system. Its two theories, namely, clonal selection and immune network theory, are integrated with PSO in this research. The clonal selection theory builds up the framework of the algorithm which consists of selection, cloning, hypermutation, memory cells extraction and receptor editing processes. Immune network theory increases the diversity of antibody set which represents the solution repertoire. To improve the antibody hypermutation process to accelerate the search procedure, a modified version of PSO is inserted. This proposed algorithm is tested on 25 benchmark problems of different sizes. The results demonstrate the effectiveness of the PSO algorithm and the specific memory cells extraction process which is one of the key features of AIS theory. By comparing with other popular approaches reported in existing literatures, this algorithm shows great competitiveness and potential, especially for small size problems in terms of computation time.     

解决作业车间调度的微粒群退火算法*

针对微粒群优化算法在求解作业车间调度问题时存在的易早熟、搜索准确度差等缺点,在微粒群优化算法的基础上引入了模拟退火算法,从而使得算法同时具有全局搜索和跳出局部最优的能力,并且增加了对不可行解的优化,从而提高了算法的搜索效率;同时,在模拟退火算法中引入自适应温度衰变系数,使得SA算法能根据当前环境自动调整搜索条件,从而避免了微粒群优化算法易早熟的缺点。对经典JSP问题的仿真实验表明,与其他算法相比,该算法是一种切实可行、有效的方法。     

A discrete version of particle swarm optimization for flowshop scheduling problems

Particle swarm optimization (PSO) is a novel metaheuristic inspired by the flocking behavior of birds. The applications of PSO to scheduling problems are extremely few. In this paper, we present a PSO algorithm, extended from discrete PSO, for flowshop scheduling. In the proposed algorithm, the particle and the velocity are redefined, and an efficient approach is developed to move a particle to the new sequence. To verify the proposed PSO algorithm, comparisons with a continuous PSO algorithm and two genetic algorithms are made. Computational results show that the proposed PSO algorithm is very competitive. Furthermore, we incorporate a local search scheme into the proposed algorithm, called PSO-LS. Computational results show that the local search can be really guided by PSO in our approach. Also, PSO-LS performs well in flowshop scheduling with total flow time criterion, but it requires more computation times.     

求解独立任务调度的离散粒子群优化算法

针对独立任务调度问题,提出一种改进的离散粒子群算法,采用基于任务的编码方式,对粒子的位置和速度更新方法进行重新定义。为防止粒子群算法的早熟收敛,给出利用模拟退火算法的局部搜索能力在最优解附近进行精细搜索,以改善解的质量。仿真结果表明,与遗传算法和基本粒子群算法相比,该混合算法具有较好的优化性能。     

混合启发式算法在排课问题上的应用

对排课问题做出了形式化描述,提出了一种用于排课的混合启发式算法,该算法合并使用了模拟退火和迭代局部搜索两种算法。先依据图着色算法产生初始可行解,然后应用模拟退火算法寻找最优解,为使算法更好地跳出局部最优,实现全局搜索,在模拟退火算法应用过程中,迭代使用两个邻域,标准邻域和双Kempe链邻域。实验结果表明,此算法能够很好地提高解的质量。