Quanke Pan

A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers

In this paper, a chaotic harmony search (CHS) algorithm is proposed to minimize makespan for the permutation flow shop scheduling problem with limited buffers. First of all, to make the harmony search algorithm suitable for solving the problem under consideration, a rank-of-value rule is applied to convert continuous harmony vectors to discrete job permutations. Secondly, an efficient initialization scheme based on the Nawaz-Enscore-Ham heuristic [M. Nawaz, E.E.J. Enscore, I. Ham, A heuristic algorithm for the m-machine, n-job flow shop sequencing problem, OMEGA-International Journal of Management Science 11 (1983) 91-95] and its variants is presented to construct an initial harmony memory with a certain level of quality and diversity. Thirdly, a new improvisation scheme is developed to well inherit good structures from the best harmony vector in the last generation. In addition, a chaotic local search algorithm with probabilistic jumping scheme is presented and embedded in the proposed CHS algorithm to enhance the local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It is shown that the proposed CHS algorithm generates better results not only than the two recently developed harmony search algorithms but also than the existing hybrid genetic algorithm and hybrid particle swarm optimization in terms of solution quality and robustness.

Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm

This paper presents a novel hybrid algorithm (TABC) that combines the artificial bee colony (ABC) and tabu search (TS) to solve the hybrid flow shop (HFS) scheduling problem with limited buffers. The objective is to minimize the maximum completion time. Unlike the original ABC algorithm, in TABC, each food source is represented by a string of job numbers. A novel decoding method is embedded to tackle the limited buffer constraints in the schedules generated. Four neighborhood structures are embedded to balance the exploitation and exploration abilities of the algorithm. A TS-based self-adaptive neighborhood strategy is adopted to impart to the TABC algorithm a learning ability for producing neighboring solutions in different promising regions. Furthermore, a well-designed TS-based local search is developed to enhance the search ability of the employed bees and onlookers. Moreover, the effect of parameter setting is investigated by using the Taguchi method of design of experiment (DOE) to determine the suitable values for key parameters. The proposed TABC algorithm is tested on sets of instances with large scales that are generated based on realistic production. Through a detailed analysis of the experimental results, the highly effective and efficient performance of the proposed TABC algorithm is contrasted with the performance of several algorithms reported in the literature.

An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation

Abstract Migrating birds optimisation (MBO) is a new nature-inspired metaheuristic for combinatorial optimisation problems. This paper proposes an improved MBO to minimise the total flowtime for a hybrid flowshop scheduling problem, which has important practical applications in modern industry. A diversified method is presented to form an initial population spread out widely in solution space. A mixed neighbourhood is constructed for the leader and the following birds to easily find promising neighbouring solutions. A leaping mechanism is developed to help MBO escape from suboptimal solutions. Problem-specific heuristics and local search procedures are added to enhance the MBO’s intensification capability. Extensive comparative evaluations are conducted with seven recently published algorithms in the literature. The results indicate that the proposed MBO is effective in comparison after comprehensive computational and statistical analyses.

Harmony search algorithm with dynamic control parameters

Abstract Harmony search (HS) is a population-based meta-heuristic imitating the music improvisation process, which has been successfully applied to optimization problems in recent years. This paper presents an effective harmony search algorithm for solving global continuous optimization problems. The proposed method presents a novel improvisation process which is different from the classical HS in two aspects. Firstly, the candidate harmony is chosen from the harmony memory by a tournament selection rule, so that the harmonies with better fitness will have more opportunities to be used in generating new harmonies. Secondly, two key control parameters, pitch adjustment rate ( PAR ) and bandwidth distance ( bw ), are adjusted dynamically with respect to the evolution of the search process and the different search spaces of the optimization problems. Numerical results demonstrate that the proposed algorithm performs much better than the existing HS variants in terms of the solution quality and the stability.

An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping

In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.

An effective hybrid harmony search-based algorithm for solving multidimensional knapsack problems

This above fig illustrates the flowchart of our proposed algorithm. The procedure consists of two main processes: the global HS-based search process and the local FFO-based search process. The FFO scheme is integrated into the modified HS as a local search strategy. Obviously, the HMs are updated two times in each iteration, one time is for the global HS search, while the other is for the local FFO search strategy. In this way, the exploration capability of the HS scheme and the exploitation ability of the FFO scheme are both considered in this algorithm. A harmony memory consideration rule is developed.Global-best pitch adjustment rule and parallel updating strategy are employed.The fruit fly optimization (FFO) scheme is integrated into the improved HS as a local search strategy. This study presents an effective hybrid algorithm based on harmony search (HHS) for solving multidimensional knapsack problems (MKPs). In the proposed HHS algorithm, a novel harmony improvisation mechanism is developed with the modified memory consideration rule and the global-best pitch adjustment scheme to enhance the global exploration. A parallel updating strategy is employed to enrich the harmony memory diversity. To well balance the exploration and the exploitation, the fruit fly optimization (FFO) scheme is integrated as a local search strategy. For solving MKPs, binary strings are used to represent solutions and two repair operators are applied to guarantee the feasibility of the solutions. The HHS is calibrated based on the Taguchi method of design-of-experiment. Extensive numerical investigations based on well-known benchmark instances are conducted. The comparative evaluations indicate the HHS is much more effective than the existing HS and FFO variants in solving MKPs.

A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems

This paper presents a hybrid Pareto-based local search (PLS) algorithm for solving the multi-objective flexible job shop scheduling problem. Three minimisation objectives are considered simultaneously, i.e. the maximum completion time (makespan), the total workload of all machines, and the workload of the critical machine. In this study, several well-designed neighbouring approaches are proposed, which consider the problem characteristics and thus can hold fast convergence ability while keep the population with a certain level of quality and diversity. Moreover, a variable neighbourhood search (VNS) based self-adaptive strategy is embedded in the hybrid algorithm to utilise the neighbouring approaches efficiently. Then, an external Pareto archive is developed to record the non-dominated solutions found so far. In addition, a speed-up method is devised to update the Pareto archive set. Experimental results on several well-known benchmarks show the efficiency of the proposed hybrid algorithm. It is concluded that the PLS algorithm is superior to the very recent algorithms, in term of both search quality and computational efficiency.

Multi-objective optimization based reverse strategy with differential evolution algorithm for constrained optimization problems

We rebuilt the model of constrained optimization problems, called reversed model.We developed a comparison strategy based on origin and new model.Difference between usual algorithm and proposed one is discussed.Experimental results show the effectiveness of the proposed algorithm. Solving constrained optimization problems (COPs) has been gathering attention from many researchers. In this paper, we defined the best fitness value among feasible solutions in current population as gbest. Then, we converted the original COPs to multi-objective optimization problems (MOPs) with one constraint. The constraint set the function value f(x) should be less than or equal to gbest; the objectives are the constraints in COPs. A reverse comparison strategy based on multi-objective dominance concept is proposed. Compared with usual strategies, the innovation strategy cuts off the worse solutions with smaller fitness value regardless of its constraints violation. Differential evolution (DE) algorithm is used as a solver to search for the global optimum. The method is called multi-objective optimization based reverse strategy with differential evolution algorithm (MRS-DE). The experimental results demonstrate that MRS-DE can achieve better performance on 22 classical benchmark functions compared with several state-of-the-art algorithms.

A hybrid artificial bee colony for optimizing a reverse logistics network system

This paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm for solving the location allocation problem in reverse logistics network system. In the proposed algorithm, each solution is represented by two vectors, i.e., a collection point vector and a repair center vector. Eight well-designed neighborhood structures are proposed to utilize the problem structure and can thus enhance the exploitation capability of the algorithm. A simple but efficient selection and update approach is applied to the onlooker bee to enhance the exploitation process. A scout bee applies different local search methods to the abandoned solution and the best solution found so far, which can increase the convergence and the exploration capabilities of the proposed algorithm. In addition, an enhanced local search procedure is developed to further improve the search capability. Finally, the proposed algorithm is tested on sets of large-scale randomly generated benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed HDBAC algorithm is shown against several efficient algorithms from the literature.

A shuffled multi-swarm micro-migrating birds optimizer for a multi-resource-constrained flexible job shop scheduling problem

This paper addresses a multi-resource-constrained flexible job shop scheduling problem with makespan criterion.A dynamic shuffled multi-swarm micro-migrating birds optimizer (MBO) is proposed.Advanced technologies including micro-MBO, multi-swarm parallel search, random shuffle process, diversity controlling strategy, and adaptive search operator are introduced.The proposed algorithm outperforms the existing state-of-art algorithms in terms of solution quality and computational efforts.9 out of 10 best known solutions for the benchmarks in the literature are improved. Scheduling problems with resource constraints have been a new research trend in recent years. This paper addresses a multi-resource-constrained flexible job shop scheduling problem that is very common in semiconductor manufacturing, precision engineering, and many other modern industries. To address this important problem, a novel algorithm called the shuffled multi-swarm micro-migrating birds optimization (SM2-MBO) algorithm is presented with a two-vector representation. The SM2-MBO forms a number of micro-swarms, each of which performs its own MBO independently. A random shuffle process applied to the entire population is invoked periodically to propagate the good information that is found in some of the micro-swarms. A diverse controlling strategy based on the aging phenomenon of life is proposed to diversify the population. An adaptive search operator based on a problem-specific crossover and a two-vector crossover helps to balance exploitation and exploration. Numerical experiments and comparisons are conducted against the best performing algorithms reported in the literature for the considered problem. The results demonstrate that the proposed SM2-MBO performs significantly better than the existing algorithms in solving the multi-resource-constrained flexible job shop scheduling problem with the makespan criterion. Furthermore, the proposed SM2-MBO can improve 9 out of 10 best known solutions for the benchmark instances in the literature.