Showing papers by "Quan-Ke Pan published in 2014"

An improved fruit fly optimization algorithm for continuous function optimization problems

Abstract: This paper presents an improved fruit fly optimization (IFFO) algorithm for solving continuous function optimization problems. In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively. A new solution generating method is developed to enhance accuracy and convergence rate of the algorithm. Extensive computational experiments and comparisons are carried out based on a set of 29 benchmark functions from the literature. The computational results show that the proposed IFFO not only significantly improves the basic fruit fly optimization algorithm but also performs much better than five state-of-the-art harmony search algorithms.

A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation

Abstract: The hybrid flowshop scheduling (HFS) problem with the objective of minimising the makespan has important applications in a variety of industrial systems. This paper presents an effective discrete artificial bee colony (DABC) algorithm that has a hybrid representation and a combination of forward decoding and backward decoding methods for solving the problem. Based on the dispatching rules, the well-known NEH heuristic, and the two decoding methods, we first provide a total of 24 heuristics. Next, an initial population is generated with a high level of quality and diversity based on the presented heuristics. A new control parameter is introduced to conduct the search of employed bees and onlooker bees with the intention of balancing the global exploration and local exploitation, and an enhanced strategy is proposed for the scout bee phase to prevent the algorithm from searching in poor regions of the solution space. A problem-specific local refinement procedure is developed to search for solution space that is unexplored by the honey bees. Afterward, the parameters and operators of the proposed DABC are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is conducted, with the best performing algorithms presented for the HFS problem under consideration, and with adaptations of some state-of-the-art metaheuristics that were originally designed for other HFS problems. The results show that the proposed DABC performs much better than the other algorithms in solving the HFS problem with the makespan criterion.

An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem

Abstract: In the no-idle flowshop, machines cannot be idle after finishing one job and before starting the next one. Therefore, start times of jobs must be delayed to guarantee this constraint. In practice machines show this behavior as it might be technically unfeasible or uneconomical to stop a machine in between jobs. This has important ramifications in the modern industry including fiber glass processing, foundries, production of integrated circuits and the steel making industry, among others. However, to assume that all machines in the shop have this no-idle constraint is not realistic. To the best of our knowledge, this is the first paper to study the mixed no-idle extension where only some machines have the no-idle constraint. We present a mixed integer programming model for this new problem and the equations to calculate the makespan. We also propose a set of formulas to accelerate the calculation of insertions that is used both in heuristics as well as in the local search procedures. An effective iterated greedy (IG) algorithm is proposed. We use an NEH-based heuristic to construct a high quality initial solution. A local search using the proposed accelerations is employed to emphasize intensification and exploration in the IG. A new destruction and construction procedure is also shown. To evaluate the proposed algorithm, we present several adaptations of other well-known and recent metaheuristics for the problem and conduct a comprehensive set of computational and statistical experiments with a total of 1750 instances. The results show that the proposed IG algorithm outperforms existing methods in the no-idle and in the mixed no-idle scenarios by a significant margin.

Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm

Abstract: This paper presents an effective fruit fly optimisation algorithm (FOA) to solve the steelmaking casting problem First, we model the realistic problem as a hybrid flow shop (HFS) scheduling problem with batching in the last stage Next, the proposed FOA algorithm is applied to solve the realistic HFS problems In the proposed algorithm, each solution is represented by a fruit fly Each fruit fly first improves its status through a well-designed smell search procedure During the vision-based search procedure, the worst fruit fly in the population will be induced by the best fruit fly found thus far to improve the exploitation ability of the entire fruit fly population further To enhance the exploration ability of the proposed algorithm, in each generation, each fruit fly that has not updated its status during the last several iterations will be replaced by a newly-generated fruit fly The proposed algorithm is tested on sets of the instances that are generated based on the realistic production Moreover, the influence of the parameter setting is also investigated using the Taguchi method of the design-of-experiment (DOE) to determine the suitable values for the key parameters The results indicate that the proposed FOA is more effective than the four presented algorithms

An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem

Abstract: Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four variants of NEH heuristic to generate the initial population, and then incorporated the estimation of distribution algorithm and a mutation operator based on insertion and swap into NSGA-II to replace traditional crossover and mutation operators. Last but not least, we performed a simple and efficient restarting strategy on the population when the diversity of the population is smaller than a given threshold. We conducted a serial of experiments, and the experimental results demonstrate that the proposed algorithm outperforms the comparative algorithms.

A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem

Abstract: A hybrid variable neighborhood search (HVNS) algorithm, which combines the key characteristics of chemical-reaction optimization (CRO) and estimation of distribution (EDA), for solving the hybrid flow shop (HFS) scheduling problems. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed HVNS algorithm is shown against the best performing algorithms from the literature. We propose a novel hybrid variable neighborhood search (HVNS) algorithm for hybrid flow shop (HFS) scheduling problems.A well-designed decoding mechanism is presented to schedule jobs with more flexibility.Considering the problem structure, eight neighborhood structures are developed.A kinetic energy sensitive neighborhood change approach is proposed.A dynamic neighborhood set update mechanism is utilized.An effective EDA-based global search approach is presented. This paper proposes a hybrid variable neighborhood search (HVNS) algorithm that combines the chemical-reaction optimization (CRO) and the estimation of distribution (EDA), for solving the hybrid flow shop (HFS) scheduling problems. The objective is to minimize the maximum completion time. In the proposed algorithm, a well-designed decoding mechanism is presented to schedule jobs with more flexibility. Meanwhile, considering the problem structure, eight neighborhood structures are developed. A kinetic energy sensitive neighborhood change approach is proposed to extract global information and avoid being stuck at the local optima. In addition, contrary to the fixed neighborhood set in traditional VNS, a dynamic neighborhood set update mechanism is utilized to exploit the potential search space. Finally, for the population of local optima solutions, an effective EDA-based global search approach is investigated to direct the search process to promising regions. The proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of experimental results, the high performance of the proposed HVNS algorithm is shown in comparison with four efficient algorithms from the literature.

An iterated greedy algorithm for the hybrid flowshop problem with makespan criterion

Abstract: The main contribution of this paper is to present some novel constructive heuristics for the the hybrid flowshop scheduling (HFS) problem with the objective of minimizing the makespan for the first time in the literature. We developed the constructive heuristics based the profile fitting heuristic by exploiting the waiting time feature of the HFS problem. In addition, we also developed an IG algorithm with a simple insertion based local search for the first time in the literature, too. The benchmark suite developed for the HFS problem are used to test the performance of the constructive heuristics and the IG algorithm. The computational results show that constructive heuristics developed were able to further improve the traditional NEH heuristics for the HFS problem with makespan criterion. Furthermore, with a very short CPU times of 50nm miliseconds, the performance of the IG algorithm was very competitive to the PSO and AIS algorithms that were run for 1600 seconds.

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

Abstract: A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Neron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.

Solving complex task scheduling by a hybrid genetic algorithm

Abstract: The Internet-of-Things (IoT) aims to connect everything on the Internet. One main advantage of IoT is the task assignment among entities. The task scheduling in IoT is very complex because there exist complex relationship between devices. In this study, we introduce the task scheduling problem with multiple processing sequence relation constraints in IoT system. Several benchmarks are given in this paper, the corresponding Gantt charts are displayed as well.

Scheduling a real-world hybrid flow shop with variable processing times using Lagrangian relaxation

Abstract: This paper studies a real-world hybrid flow shop problem arising from the steelmaking continuous casting process, which is the bottleneck of the iron and steel production. There are a variety of features to be taken into account, in particular the batch constraints and the variable processing times in the last stages. Based on a time-index formulation and machine capacity relaxation, three Lagrangian relaxation (LR) approaches are presented for addressing this scheduling problem. The three LR approaches decompose the relaxed problem into job-level problems, batch-level problems and machine-level problems, respectively. These subproblems are solved based on polynomial dynamic programming algorithms. The corresponding Lagrangian dual (LD) problems are solved by an efficient subgradient algorithm with global convergence. Computational results and comparisons demonstrate that the approach adopting job-level decomposition is most efficient among three approaches, whereas the approach adopting batch-level decomposition is most effective.

An improved Harmony search algorithm with dynamic control parameters for continuous optimization problems

Abstract: An improved harmony search algorithm is presented for solving continuous optimization problems in this paper. In the proposed algorithm, an elimination principle is developed for choosing from the harmony memory, so that the harmonies with better fitness will have more opportunities to be selected in generating new harmonies. Two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process with the different search spaces of the optimization problems. Numerical results of 12 benchmark problems show that the proposed algorithm performs more effectively than the existing HS variants in finding better solutions.

Design and development of optimization software of magnetic multi-process coordination production planning based on benefit

Abstract: It is essential to effective control plant inventory, improve production efficiency, reduce production cost and raise product delivery time satisfactory rate that the fast and effective optimization software's application of magnetic multi-process coordination production planning. This paper researches the integration of production planning problem to solve multi-process, multi-device, multi-stage's planning, which based on theory of constraints (TOC).The planning problem takes enterprise benefit as goal and takes bottleneck resources in the production process as system constraints. Then we change the research result into optimization software of magnetic multi-process coordination production planning. The practical application of the software demonstrates the design and development of this software's feasibility and necessity.

A hybrid Pareto-based algorithm for multi-objective resource allocation problem

Abstract: This paper introduces a hybrid algorithm combining discrete harmony search (DHS) and iterated local search (ILS) for solving the multi-objective resource allocation problem (RAP). Two objectives are considered simultaneously, i.e. minimization of the overall cost and overall efficiency. The harmony search algorithm is used to conduct the global exploration task, while the iterated local search performs the exploitation work. In addition, an external Pareto archive set was introduced to memory the non-dominated solutions found so far. Experimental results on the well-known benchmarks verify the efficiency and effectiveness of the propose algorithm.

Magnetic material production charge design method

Abstract: The invention provides a magnetic material production charge design method. The method includes the steps that firstly, initial information of magnetic material production charge design is collected; secondly, according to the initial information, an optimized objective function and constraint conditions of the optimized objective function of magnetic material production charge design are established, an initial magnetic material production charge design scheme is determined, the scheme is optimized and adjusted, and then the optimized magnetic material production charge design scheme is acquired; thirdly, melt-spinning furnace production is conducted according to the optimized magnetic material production charge design scheme. The priority of a magnetic material production work order, the mark of the magnetic material production work order, the date of delivery of the magnetic material production work order and the demand for yield of the magnetic material production work order of magnetic material production charge design, the upper limit of melt-spinning furnace production, the lower limit of the melt-spinning furnace production and the like are fully taken into consideration, production efficiency of a melt-spinning furnace is fully dug, working strength of schemers is relieved, efficiency of magnetic material production charge design is improved, work order delivery punctuality is improved, and economic benefits of enterprises are increased.