Junbiao Wang

A self-adaptive harmony PSO search algorithm and its performance analysis

A self-adaptive harmony particle swarm optimization search algorithm is proposed.PSO algorithm is utilized to initial the harmony memory (HM).Pitch adjusting rate (PAR) and distance bandwidth (bw), are adjusted dynamically.A Gaussian mutation operator is added to reinforce the robustness.The convergence of the SHPSOS algorithm has been proved theoretically. Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHPSOS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the PSO algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHPSOS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHPSOS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHPSOS outperforms original HS and the other related algorithms in terms of the solution quality and the stability.

Theory of constraints product mix optimisation based on immune algorithm

Product mix optimisation is one of the most fundamental problems in manufacturing enterprise. As an important component in theory of constraints (TOC), product mix optimisation is solved by the TOC heuristic (TOCh) and some intelligent search algorithms, even though these approaches often cannot effectively obtain a good solution in the previous attempts, especially for the large-scale product mix optimisation. Aiming at this problem, a contribution has been made to the following aspects in the present paper. Firstly, a model of TOC product mix optimisation, which identifies and exploits the capacity constrained resource (CCR) to maximise system throughput is put forward and simplified by cutting down some constraints of non-CCRs. Secondly, an intelligent optimisation approach based on immune algorithm (IA) and TOC for product mix optimisation is presented to search optimal solution(s), whether it is a small-scale or large-scale instance. Thirdly, the immune mechanisms, such as the immune response mechanism, immune self-adaptive regulation and vaccination, are studied in detail, which not only greatly improves the searching ability and adaptability, but also evidently increases the global convergence rate of immune evolution. Fourthly, the proposed approach is implemented and applied in both small-scale and large-scale product mix optimisation. Finally, a comparison between the proposed approach and existing approaches is made. Simulation results show that the proposed approach is superior to the existing approaches, such as the TOCh, revised TOCh, integer linear programming (ILP), tabu search (TS), and genetic algorithms (GA).

A shuffled complex evolution algorithm with opposition-based learning for a permutation flow shop scheduling problem

The permutation flow shop scheduling problem (PFSP) is a typical non-deterministic polynomial-time hard problem, which has wide engineering applications, and performs an important function in manufacturing fields. In this paper, an improved shuffled complex evolution algorithm with opposition-based learning (SCE-OBL) was proposed to obtain the optimal makespan for permutation flow shop scheduling. The OBL strategy was used to improve the population quality and accelerate the convergence speed. The theoretical analysis demonstrated that the improved algorithm converged to optimum with a probability of 1. Moreover, the largest-order-value mechanism was used in the combinational optimisation problem to change the variables in the continuous domain to discrete variables, and job-based representation was adopted for encoding the solution of the PFSP. Twenty-nine typical instances were then used to test the performance of the SCE-OBL, and the computational results showed that the SCE-OBL algorithm could obtain better solutions for the PFSP than other algorithms.

An improved particle swarm optimisation with a linearly decreasing disturbance term for flow shop scheduling with limited buffers

The flow shop scheduling problem with limited buffers is a typical combinational optimisation problem that is NP-hard. In this article, an improved particle swarm optimisation with a linearly decreasing disturbance term (LDPSO) is presented for permutation flow shop scheduling with limited buffers between consecutive machines to minimise the maximum completion time (i.e. the makespan). A linearly decreasing disturbance term was added to the velocity, updating formula of the standard particle swarm optimisation algorithm. The decision probability of the linearly decreasing disturbance term was used to control the utilisation of the global exploration operation and the local exploitation search based on problem-specific information so as to prevent premature convergence and concentrate computing efforts on promising neighbour solutions. Theoretical analysis based on previous studies showed that the improved algorithm converged to the global optimum at a probability of 1. The ranked-order-value encoded method transferred the continuous particle position of the LDPSO to the order sequence. Furthermore, the neighbour search strategy based on block guaranteed that the entire order sequence could be searched. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the LDPSO. The effects of buffer size and decision probability on optimisation performance are discussed in this article.

A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of Estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.

An improved shuffled complex evolution algorithm with sequence mapping mechanism for job shop scheduling problems

An Improved Shuffled Complex Evolution (ISCE) algorithm is proposed.The sequence mapping mechanism was presented.The sequence with job permutation, is adopted for encoding and decoding.A new strategy is used to improve the individuals evolution to overcome stagnation.The results show that the improved algorithm is effective to the job shop scheduling. The job shop problem is an important part of scheduling in the manufacturing industry. A new intelligent algorithm named Shuffled Complex Evolution (SCE) algorithm is proposed in this paper with the aim of getting the minimized makespan. The sequence mapping mechanism is used to change the variables in the continuous domain to discrete variables in the combinational optimization problem; the sequence, which is based on job permutation, is adopted for encoding mechanism and sequence insertion mechanism for decoding. While considering that the basic SCE algorithm has the drawbacks of poor solution and lower rate of convergence, a new strategy is used to change the individuals evolution in the basic SCE algorithm. The strategy makes the new individual closer to best individual in the current population. The improved SCE algorithm (ISCE) was used to solve the typical job shop problems and the results show that the improved algorithm is effective to the job shop scheduling.

A chemotaxis-enhanced bacterial foraging algorithm and its application in job shop scheduling problem

In this article, a chemotaxis-enhanced bacterial foraging optimisation (CEBFO) is proposed to solve the job shop scheduling problem more effectively. The new approach, which is based on a new chemotaxis with the differential evolution (DE) operator added, aims at solving the tumble failure problem in the tumble step and accelerates the convergence speed of the original algorithm. The effectiveness of the new chemotaxis and the convergence are proved theoretically and tested in continuous problems. Furthermore, a local search operator was designed, which can improve the local search ability of novel algorithm greatly. Finally, the experiments were conducted on a set of 38 benchmark problems of job shop scheduling and the results demonstrated the outperformance of the proposed algorithm.

A chaotic local search based bacterial foraging algorithm and its application to a permutation flow-shop scheduling problem

The permutation flow-shop scheduling problem (PFSP) is a typical combinational and non-deterministic polynomial-hard problem, with discrete solution space. In this paper, a novel bacterial foraging optimisation algorithm (BFO) has been proposed to solve the PFSP. Difficulties such as slow convergent speeds and entrapment in the local optimum were incurred by the original BFO algorithm in solving a high-dimensional combinatorial optimisation problem. In order to deal with these difficulties, a differential evolution operator and a chaotic search operator were each introduced into the original BFO algorithm to enhance the activity levels of the individual bacterium and to extend the local searching space. Theoretical analysis showed that the improved algorithm obtained more motility in chemotaxis and could converge to the global optimum with a probability of 1. Simulation results and comparisons to both continuous and combinatorial benchmark problems were used to demonstrate the effectiveness of this novel optimisation algorithm.