Ant colonies with cooperative process applied to the resource allocation problem

Abstract

In this paper, a novel algorithm describing ant colonies, with cooperation, is proposed to solve the resource allocation problem. The resource allocation problem is to allocate resources to activities, with the objective of optimizing the cost function. In our study, we viewed the search in ant colonies as a mechanism providing a main portion of diversity in search space. The cooperative process conducts fine‐tuning for the solution provided by ant colonies, and it has the ability to escape from poor local optima. In this paper, several examples are tested to prove the superiority of our proposed algorithm. From simulation results, the proposed algorithm indeed has remarkable performance.     

A genetic algorithm with domain knowledge for weapon‐target assignment problems

Abstract

In this paper, a novel genetic algorithm, including domain specific knowledge into the crossover operator and the local search mechanism for solving weapon‐target assignment (WTA) problems is proposed. The WTA problem is a full assignment of weapons to hostile targets with the objective of minimizing the expected damage value to own‐force assets. It is an NP‐complete problem. In our study, a greedy reformation and a new crossover operator are proposed to improve the search efficiency. The proposed algorithm outperforms its competitors on all test problems.     

Neighborhood search algorithms with restricted infeasible solution sets–application to a transportation project evaluation problem

Abstract

Using a transportation project evaluation problem as an example, in this paper we employ the local search method, the threshold accepting method, together with the combination of feasible and restricted infeasible solution sets in neighborhood searches, to develop four solution algorithms. The test results indicate that the threshold accepting algorithm and the local search algorithm, that combine feasible and restricted infeasible solution sets, can improve the conventional threshold accepting algorithm and local search algorithm, which are confined to feasible solution sets.     

An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.     

Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times

In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used in GAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into the GAs with a minimum relocation operation of the genes’ random key numbers – this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm.     

图的度量维数问题的0-1蚁群条件着色分辨算法研究

图的度量维数问题（MDP）是一类在机器导航、声呐系统布置、化学、数据分类等领域有重要应用的组合优化问题．针对该问题,本文通过引入图的分辨表存储结构,建立了非线性求解模型;同时,通过改进现有蚁群算法的参数设计,利用全局搜索和局部搜索相结合的策略,建立了求解模型的改进型蚁群算法．数值对比分析验证了算法的有效性：全局搜索和局部搜索的结合较大程度的改进了算法求解质量;在规则图上提高算法求解质量具有一定挑战;与遗传算法计算结果相比较,本文提出的算法不仅在求解质量方面有所提升,而且在最坏的情况下能为图提供极小分辨集. 最后,本文探索了部分算法参数对算法求解质量的影响,并给出了进一步研究课题．     

A hybrid electromagnetism-like algorithm for two-stage assembly flow shop scheduling problem

This paper presents a study on the two-stage assembly flow shop scheduling problem for minimising the weighed sum of maximum makespan, earliness and lateness. There are m machines at the first stage, each of which produces a component of a job. A single machine at the second stage assembles the m components together to complete the job. A novel model for solving the scheduling problem is built to optimise the maximum makespan, earliness and lateness simultaneously. Two optimal operation sequences of jobs are determined and verified. As the problem is known to be NP-hard, a hybrid variable neighbourhood search – electromagnetism-like mechanism (VNS-EM) algorithm is proposed for its handling. To search beyond local optima for a global one, VNS algorithm is embedded in each iteration of EM, whereby the fine neighbourhood search of optimum individuals can be realised and the solution is thus optimised. Simulation results show that the proposed hybrid VNS-EM algorithm outperforms the EM and VNS algorithms in both average value and standard deviation.     

Effective hybrid genetic algorithm for minimizing makespan on a single-batch-processing machine with non-identical job sizes

The paper addresses minimizing makespan by a genetic algorithm (GA) for scheduling jobs with non-identical sizes on a single-batch-processing machine. A batch-processing machine can process up to B jobs simultaneously. The processing time of a batch is equal to the longest processing time among all jobs in the batch. Two different GAs are proposed based on different encoding schemes. The first is a sequence-based GA (SGA) that generates random sequences of jobs using GA operators and applies the batch first fit heuristic to group the jobs. The second is a batch-based hybrid GA (BHGA) that generates random batches of jobs using GA operators and ensures feasibility by using knowledge of the problem based on a heuristic procedure. A greedy local search heuristic based on the problem characteristics is hybridized with a BHGA that has the ability of steering efficiently the search toward the optimal or near-optimal schedules. The performance of proposed GAs is compared with a simulated annealing (SA) approach proposed by Melouk et al. (Melouk, S., Damodaran, P. and Chang, P.Y., Minimizing makespan for single machine batch processing with non-identical job sizes using simulated annealing. Int. J. Prod. Econ., 2004, 87, 141–147) and also against a modified lower bound proposed for the problem. Computational results show that BHGA performs considerably well compared with the modified lower bound and significantly outperforms the SGA and SA in terms of both quality of solutions and required runtimes.     

A tabu search approach for buffer allocation in production lines with unreliable machines

The optimal allocation of buffers is an important research issue in designing production lines. In this study, a tabu search (TS) algorithm is proposed to find near-optimal buffer allocation plans for a serial production line with unreliable machines. The main objective is to maximize the production rate, i.e. throughput, of the line. The efficiency of the proposed method is also tested to solve buffer allocation problems with the objective of total buffer size minimization. To estimate the throughput of the line with a given specific buffer allocation, an analytical decomposition approximation method is used. The performance of the tabu search algorithm is demonstrated on existing benchmark problems. The results obtained by the TS algorithm are clearly encouraging, as the TS algorithm is much better than the other algorithms for all considered benchmark problems.     

Parallel machine scheduling with step-deteriorating jobs and setup times by a hybrid discrete cuckoo search algorithm

This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup–Hermann–Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX^® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.     

Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness

This paper studies the problem of scheduling flexible job shops with setup times where the setups are sequence-dependent. The objective is to find the schedule with minimum total tardiness. First, the paper develops a mathematical model in the form of mixed integer linear programming and compares it with the available model in the literature. The proposed model outperforms the available model in terms of both size complexity and computational complexity. Then, an effective metaheuristic algorithm based on iterated local search is proposed and compared with a tabu search and variable neighbourhood search algorithms proposed previously for the same problem. A complete experiment is conducted to evaluate the algorithms for performance. All the results show the superiority of the proposed algorithm against the available ones.     

A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking

A flow-shop scheduling problem with blocking has important applications in a variety of industrial systems but is underrepresented in the research literature. In this study, a novel discrete artificial bee colony (ABC) algorithm is presented to solve the above scheduling problem with a makespan criterion by incorporating the ABC with differential evolution (DE). The proposed algorithm (DE-ABC) contains three key operators. One is related to the employed bee operator (i.e. adopting mutation and crossover operators of discrete DE to generate solutions with good quality); the second is concerned with the onlooker bee operator, which modifies the selected solutions using insert or swap operators based on the self-adaptive strategy; and the last is for the local search, that is, the insert-neighbourhood-based local search with a small probability is adopted to improve the algorithm's capability in exploitation. The performance of the proposed DE-ABC algorithm is empirically evaluated by applying it to well-known benchmark problems. The experimental results show that the proposed algorithm is superior to the compared algorithms in minimizing the makespan criterion.     

Delivery of pre‐stored videos for mobile clients with insufficient playback buffer and bounded delay guarantee

Abstract

A deterministic service model assisted by a sufficiently large playback buffer space can provide bounded delay guarantees for video packets and simplify network resource management. However, many popular mobile terminals do not have sufficient memory capacity for deterministic video services since the embedded memory is limited and needs to be shared by numerous software programs and masses of personal data. This paper improves the traditional deterministic modeling approach for delivering pre‐stored videos to mobile clients with QoS guarantees. The limitation of playback buffer space, the network delay jitter, the processing load of resource management, and the QoS guarantee are considered in the proposed mechanism. Some traffic smoothing operations are integrated into the proposed mechanism for reducing the playback buffer demand and data rate variation. This paper further proposes a smart video frame skip algorithm, originating at the sender for preventing possible overflow problems due to insufficient playback buffer space. The algorithm can determine the most suitable temporal range for skipping frames and prevent arbitrary discarding from inappropriate video frames such as I‐frames on the client side. Simulation results reveal that the proposed mechanism can effectively remedy situations of insufficient playback buffer space while still maintaining the advantages of deterministic services.     

Nondestructive identification of multiple flaws using XFEM and a topologically adapting artificial bee colony algorithm

We present a novel algorithm based on the extended finite element method (XFEM) and an enhanced artificial bee colony (EABC) algorithm to detect and quantify multiple flaws in structures. The concept is based on recent work that have shown the excellent synergy between XFEM, used to model the forward problem, and a genetic‐type algorithm to solve an inverse identification problem and converge to the ‘best’ flaw parameters. In this paper, an adaptive algorithm that can detect multiple flaws without any knowledge on the number of flaws beforehand is proposed. The algorithm is based on the introduction of topological variables into the search space, used to adaptively activate/deactivate flaws during run time until convergence is reached. The identification is based on a limited number of strain sensors assumed to be attached to the structure surface boundaries. Each flaw is approximated by a circular void with the following three variables: center coordinates (x_c, y_c) and radius (r_c), within the XFEM framework. In addition, the proposed EABC scheme is improved by a guided‐to‐best solution updating strategy and a local search (LS) operator of the Nelder–Mead simplex type that show fast convergence and superior global/LS abilities compared with the standard ABC or classic genetic algorithms. Several numerical examples, with increasing level of difficulty, are studied in order to evaluate the proposed algorithm. In particular, we consider identification of multiple flaws with unknown a priori information on the number of flaws (which makes the inverse problem harder), the proximity of flaws, flaws having irregular shapes (similar to artificial noise), and the effect of structured/unstructured meshes. The results show that the proposed XFEM–EABC algorithm is able to converge on all test problems and accurately identify flaws. Hence, this methodology is found to be robust and efficient for nondestructive detection and quantification of multiple flaws in structures. Copyright © 2013 John Wiley & Sons, Ltd.     

A two-machine permutation flow shop scheduling problem with buffers

Problems with blocking (limited intermediate storage space) are used frequently for modelling and scheduling just-in-time and flexible manufacturing systems. In this paper, an approximation algorithm is presented for the problem of finding the minimum makespan in a two-machine permutation flow-shop scheduling problem with the mediating buffer of finite capacity. The algorithm is based on the tabu search approach supported by the reduced neighborhood, search accelerator and technique of back jumps on the search trajectory. Due to some special properties, the proposed algorithm provides makespans very close to optimal in a short time. It has been shown that this algorithm outperforms all known approximation algorithms for the problem stated.     

Flow-shop problems with intermediate buffers

In this paper the following extension of the classical flow-shop problem is considered: Between each two consecutive machines a buffer of limited capacity is given. After finishing processing on a machine, a job either directly has to be processed on the following machine or it has to be stored in the buffer between these machines. If the buffer is completely occupied the job may wait on its current machine but blocks this machine for other jobs. The objective is to determine a feasible schedule minimizing the makespan. To model such a problem setting, a variation of the classical disjunctive graph model for shop problems is extended. A tabu search procedure is described where neighborhoods based on an extension of the classical block approach theorem are used. Computational results for extended flow-shop benchmark instances are presented. Corespondence to: Silvia HeitmannThe authors are grateful to Tim Nieberg for implementing the tabu search procedure proposed in this paper.S. Heitmann     

The Statistical Filter Approach to Constrained Optimization

Abstract

Expensive black box systems arise in many engineering applications but can be difficult to optimize because their output functions may be complex, multi-modal, and difficult to understand. The task becomes even more challenging when the optimization is subject to multiple constraints and no derivative information is available. In this article, we combine response surface modeling and filter methods in order to solve problems of this nature. In employing a filter algorithm for solving constrained optimization problems, we establish a novel probabilistic metric for guiding the filter. Overall, this hybridization of statistical modeling and nonlinear programming efficiently utilizes both global and local search in order to quickly converge to a global solution to the constrained optimization problem. To demonstrate the effectiveness of the proposed methods, we perform numerical tests on a synthetic test problem, a problem from the literature, and a real-world hydrology computer experiment optimization problem.     

Hybrid algorithm based on improved extended shifting bottleneck procedure and GA for assembly job shop scheduling problem

With the makespan as the optimisation goal, we propose a hybrid solving method that combines improved extended shifting bottleneck procedure (i-ESB) and genetic algorithm (GA) for the assembly job shop scheduling problem (AJSSP). Hybrid genetic algorithm (HGA) uses a GA based on operation constraint chain coding to achieve global search and a local search based on an i-ESB. In the design of i-ESB, an extended disjunctive graph model (EDG) corresponding to AJSSP is presented. The calculation method of the operation head and tail length based on EDG is studied, as well as the searching method of key operations. The Schrage algorithm with disturbance is used to solve the single-machine scheduling subproblem. The selection criterion for bottleneck machines is increased. A greedy bottleneck machine re-optimisation process is designed. The effectiveness and superiority of the proposed algorithm are verified by testing and analysing the relevant examples in the literature.     

Re-entrant flow shop scheduling problem with time windows using hybrid genetic algorithm based on auto-tuning strategy

The re-entrant flow shop scheduling problem considering time windows constraint is one of the most important problems in hard-disc drive (HDD) manufacturing systems. In order to maximise the system throughput, the problem of minimising the makespan with zero loss is considered. In this paper, evolutionary techniques are proposed to solve the complex re-entrant scheduling problem with time windows constraint in manufacturing HDD devices with lot size. This problem can be formulated as a deterministic Fm?|?fmls, rcrc, temp?|?C_max problem. A hybrid genetic algorithm was used for constructing chromosomes by checking and repairing time window constraints, and improving chromosomes by a left-shift heuristic as a local search algorithm. An adaptive hybrid genetic algorithm was eventually developed to solve this problem by using fuzzy logic control in order to enhance the search ability of the genetic algorithm. Finally, numerical experiments were carried out to demonstrate the efficiency of the developed approaches.     

基于IHHO算法的光伏电池工程模型的参数辨识

针对HHO算法存在搜索过程调整不够灵活,不能针对性地进行阶段性搜索,有时会陷入局部最优使算法搜索精度相对较差等问题,提出了一种基于改进哈里斯鹰优化(IHHO)算法的参数辨识方法.对HHO算法进行了两项改进:引人柔性递减策略,在迭代初期扩大全局搜索范围,在迭代后期延长局部搜索时间,从而加强了初期的全局搜索能力和后期的局部...