Hong-yan Sang

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 High Performing Memetic Algorithm for the Flowshop Scheduling Problem With Blocking

This paper considers minimizing makespan for a blocking flowshop scheduling problem, which has important application in a variety of modern industries. A constructive heuristic is first presented to generate a good initial solution by combining the existing profile fitting (PF) approach and Nawaz-Enscore-Ham (NEH) heuristic in an effective way. Then, a memetic algorithm (MA) is proposed including effective techniques like a heuristic-based initialization, a path-relinking-based crossover operator, a referenced local search, and a procedure to control the diversity of the population. Afterwards, the parameters and operators of the proposed MA are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is carried out with the best performing algorithms presented for the blocking flowshop with makespan criterion, and with the adaptations of other state-of-the-art MAs originally designed for the regular flowshop problem. The results show that the proposed MA performs much better than the other algorithms. Ultimately, 75 out of 120 upper bounds provided by Ribas [“An iterated greedy algorithm for the flowshop scheduling with blocking”, OMEGA, vol. 39, pp. 293-301, 2011.] for Taillard flowshop benchmarks that are considered as blocking flowshop instances are further improved by the presented MA.

Honey bees mating optimization algorithm for process planning problem

Process planning is a very important function in the modern manufacturing system. It impacts the efficiency of the manufacturing system greatly. The process planning problem has been proved to be a NP-hard problem. The traditional algorithms cannot solve this problem very well. Therefore, due to the intractability and importance of process planning problem, it is very necessary to develop efficiency algorithms which can obtain a good process plan with minimal global machining cost in reasonable time. In this paper, a new method based on honey bees mating optimization (HBMO) algorithm is proposed to optimize the process planning problem. With respect to the characteristics of process planning problem, the solution encoding, crossover operator, local search strategies have been developed. To evaluate the performance of the proposed algorithm, three experiments have been carried out, and the comparisons among HBMO and some other existing algorithms are also presented. The results demonstrate that the HBMO algorithm has achieved satisfactory improvement.

An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems

Lot-streaming scheduling problem has been an active area of research due to its important applications in modern industries. This paper deals with the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion. An effective discrete invasive weed optimization (DIWO) algorithm is presented with new characteristics. A job permutation representation is utilized and an adapted Nawaz–Enscore–Ham heuristic is employed to ensure an initial weed colony with a certain level of quality. A new spatial dispersal model is designed based on the normal distribution and the property of tangent function to enhance global search. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. The presented DIWO is calibrated by means of the design of experiments approach. A comparative evaluation is carried out with several best performing algorithms based on a total of 280 randomly generated instances. The numerical experiments show that the presented DIWO algorithm produces significantly better results than the competing algorithms and it constitutes a new state-of-the-art solution for the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion.

Discrete artificial bee colony algorithm for lot-streaming flowshop with total flowtime minimization

Unlike a traditional flowshop problem where a job is assumed to be indivisible, in the lot-streaming flowshop problem, a job is allowed to overlap its operations between successive machines by splitting it into a number of smaller sub-lots and moving the completed portion of the sub-lots to downstream machine. In this way, the production is accelerated. This paper presents a discrete artificial bee colony (DABC) algorithm for a lot-streaming flowshop scheduling problem with total flowtime criterion. Unlike the basic ABC algorithm, the proposed DABC algorithm represents a solution as a discrete job permutation. An efficient initialization scheme based on the extended Nawaz-Enscore-Ham heuristic is utilized to produce an initial population with a certain level of quality and diversity. Employed and onlooker bees generate new solutions in their neighborhood, whereas scout bees generate new solutions by performing insert operator and swap operator to the best solution found so far. Moreover, a simple but effective local search is embedded in the algorithm to enhance local exploitation capability. A comparative experiment is carried out with the existing discrete particle swarm optimization, hybrid genetic algorithm, threshold accepting, simulated annealing and ant colony optimization algorithms based on a total of 160 randomly generated instances. The experimental results show that the proposed DABC algorithm is quite effective for the lot-streaming flowshop with total flowtime criterion in terms of searching quality, robustness and effectiveness. This research provides the references to the optimization research on lot-streaming flowshop.

An effective modified migrating birds optimization for hybrid flowshop scheduling problem with lot streaming

This above figure illustrates the flowchart of our proposed algorithm (EMBO). The proposed algorithm starts with a number of initial solutions randomly generated in the solution space, including a leader solution and the other members in left and right lines. Then, an evolving loop involving a number of tours proceeds, and each tour evolves beginning with the leader and processing along the left and right lines in parallel by exploring their neighborhood and using the dynamic solution acceptance criteria. The insertion and the pairwise exchange neighborhood operators are respectively applied for the individuals in PL and PR. And two competitive mechanisms are used to modify the solutions order when a tour is finished. Finally, when a loop is finished, the scout phase for the solutions is conducted, the leader is to be changed, and another loop starts.Display Omitted The problem of hybrid flowshop hybridizing with lot streaming is addressed.A shortest waiting time rule is introduced to schedule the jobs concurrently arriving.The dynamic solution acceptance criteria is developed. In this paper, the problem of hybrid flowshop hybridizing with lot streaming (HLFS) with the objective of minimizing the total flow time is addressed. We propose a mathematical model and an effective modified migrating birds optimization (EMBO) to solve this problem within an acceptable computational time. A so-called shortest waiting time rule (SWT) is introduced to schedule the jobs concurrently arriving at stages more reasonably. A combined neighborhood search strategy is developed that unites two different neighborhood operators during evolution, not only taking full advantage of their specializations but also promoting their joint efforts. Two competitive mechanisms are respectively used to increase the probability of locating better solutions at the front of the flock and enhance the interaction between two lines. The scout phase on the basis of the Glover operator and a well-designed local search is applied to the individuals trapped into local optimums and helps the algorithm explore potential promising domains. The dynamic solution acceptance criteria is developed to strike a compromise between intensification and diversification mechanisms. The performance of our proposed algorithm is evaluated by comparisons with seven other efficient algorithms in the literature. And the extensive numerical illustrations demonstrate that the proposed algorithm performs much more effectively for the addressed problem.

An improved migrating birds optimization for an integrated lot-streaming flow shop scheduling problem

Abstract Lot-streaming is an effective technology to enhance the production efficiency by splitting a job or a lot into several sublots. It is commonly assumed that lot-splitting (i.e. job-splitting) is specified in advance and fixed during the optimization procedure in recent studies on lot-streaming flow shop scheduling problems. In many real-world production processes, however, it is not easy to determine the optimal lot-splitting beforehand. Therefore, in this paper we consider an integrated lot-streaming flow shop scheduling problem in which lot-splitting and job scheduling are needed to be optimized simultaneously. We provide a mathematical model for the problem and present an improved migrating birds optimization (IMMBO) to minimize the maximum completion time or makespan. In the IMMBO algorithm, a harmony search based scheme is designed to construct neighborhood of solutions, which makes good use of optimization information from the population and can tune the search scope adaptively. Moreover, a leaping mechanism is introduced to avoid being trapped in the local optimum. Extensive numerical simulations are conducted and comparisons with other state-of-the-art algorithms verify the effectiveness of the proposed IMMBO algorithm.

An Iterated Local Search Algorithm for the Lot-Streaming Flow Shop Scheduling Problem

The lot-streaming flow shop scheduling problem plays an important role in modern industry. This paper addresses this problem with the objective of minimizing the total weighted earliness and tardiness penalties and then proposes a simple but effective iterated local search (ILS) algorithm. In the proposed ILS algorithm, an adapted Nawaz–Enscore–Ham (NEH) heuristic is used to generate an initial solution. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. A simulated-annealing-typed acceptance criterion is utilized to determine the start point for next iteration. Extensive experiments are conducted to compare the proposed ILS algorithm with some existing algorithms. The computational results and comparisons demonstrate the effectiveness of the proposed ILS algorithm.

An effective invasive weed optimization algorithm for scheduling semiconductor final testing problem

Abstract In this paper, we address a semiconductor final testing problem from the semiconductor manufacturing process. We aim to determine both the assignment of machines and the sequence of operations on all the machines so as to minimize makespan. We present a cooperative co-evolutionary invasive weed optimization (CCIWO) algorithm which iterates with two coupled colonies, one of which addresses the machine assignment problem and the other deals with the operation sequence problem. To well balance the search capability of the two colonies, we adopt independent size setting for each colony. We design the reproduction and spatial dispersal methods for both the colonies by taking advantage of the information collected during the search process and problem-specific knowledge. Extensive experiments and comparison show that the proposed CCIWO algorithm performs much better than the state-of-the-art algorithms in the literature for solving the semiconductor final testing scheduling problem with makespan criteria.

A new penalty function method for constrained optimization using harmony search algorithm

This paper proposes a novel penalty function measure for constrained optimization using a new harmony search algorithm. In the proposed algorithm, a two-stage penalty is applied to the infeasible solutions. In the first stage, the algorithm can search for feasible solutions with better objective values efficiently. In the second stage, the algorithm can take full advantage of the information contained in infeasible individuals and avoid trapping in local optimum. In addition, for adapting to this method, a new harmony search algorithm is presented, which can keep a balance between exploration and exploitation in the evolution process. Numerical results of 13 benchmark problems show that the proposed algorithm performs more effectively than the ordinary methods for constrained optimization problems.