ABC-based manufacturing scheduling for unrelated parallel machines with machine-dependent and job sequence-dependent setup times

To date, the topic of unrelated parallel machine scheduling problems with machine-dependent and job sequence-dependent setup times has received relatively little research attention. In this study, a hybrid artificial bee colony (HABC) algorithm is presented to solve this problem with the objective of minimizing the makespan. The performance of the proposed HABC algorithm was evaluated by comparing its solutions to state-of-the-art metaheuristic algorithms and a high performing artificial bee colony (ABC)-based algorithm. Extensive computational results indicate that the proposed HABC algorithm significantly outperforms these best-so-far algorithms. Since the problem addressed in this study is a core topic for numerous industrial applications, this article may help to reduce the gap between theoretical progress and industrial practice.     

A hybrid artificial bee colony for a nurse rostering problem

The nurse rostering problem (NRP) is a combinatorial optimization problem tackled by assigning a set of shifts to a set of nurses, each has specific skills and work contract, to a predefined rostering period according to a set constraints. The metaheuristics are the most successful methods for tackling this problem. This paper proposes a metaheuristic technique called a hybrid artificial bee colony (HABC) for NRP. In HABC, the process of the employed bee operator is replaced with the hill climbing optimizer (HCO) to empower its exploitation capability and the usage of HCO is controlled by hill climbing rate (HCR) parameter. The performance of the proposed HABC is evaluated using the standard dataset published in the first international nurse rostering competition 2010 (INRC2010). This dataset consists of 69 instances which reflect this problem in many real-world cases that are varied in size and complexity. The experimental results of studying the effect of HCO using different value of HCR show that the HCO has a great impact on the performance of HABC. In addition, a comparative evaluation of HABC is carried out against other eleven methods that worked on INRC2010 dataset. The comparative results show that the proposed algorithm achieved two new best results for two problem instances, 35 best published results out of 69 instances as achieved by other comparative methods, and comparable results in the remaining instances of INRC2010 dataset.     

A hybrid algorithm for constrained portfolio selection problems

Since Markowitz’s seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.     

一种混合人工蜂群算法求解顺序相依拆卸线平衡问题

拆卸线平衡问题直接影响回收再制造成本.为此,构建了最小工作站开启数量、最短总拆卸时间、均衡工作站空闲时间、尽早拆卸有危害和高需求零部件的多目标顺序相依拆卸线平衡问题优化模型,提出一种混合人工蜂群算法.所提出算法在观察蜂跟随阶段采用分阶段选择评价法,以便更好地区分蜜源;在侦查蜂开采阶段构建基于全局学习的搜索机制,以提高开采能力.蜜蜂寻优过程中设计了简化变邻域搜索策略,提高了寻优效率.对比实验结果验证了模型的有效性和算法的优越性.     

A hybrid approach to artificial bee colony algorithm

In this paper, we put forward a hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation. The bee life-cycle model is firstly constructed, which means that each individual can reproduce or die dynamically throughout the searching process and population size can dynamically vary during execution. With the comprehensive learning, the bees incorporate the information of global best solution into the search equation for exploration, while the Powell’s search enables the bees deeply to exploit around the promising area. Finally, we instantiate a hybrid artificial bee colony (HABC) optimizer based on the proposed model, namely HABC. Comprehensive test experiments based on the well-known CEC 2014 benchmarks have been carried out to compare the performance of HABC against other bio-mimetic algorithms. Our numerical results prove the effectiveness of the proposed hybridization scheme and demonstrate the performance superiority of the proposed algorithm.     

A hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem

This paper proposes a hybrid bat algorithm with natural-inspired algorithms for continuous optimization problem. In this study, the proposed algorithm combines the reproduction step from weed algorithm and genetic algorithm. The reproduction step is applied to clone each bat population by fitness values and the genetic algorithm is applied in order to expand the population. The algorithm is evaluated on eighteen benchmark problems. The computational results of the proposed algorithm are compared with the methods in the literature which are self-adaptive differential evolution (DE), traditional DE algorithm, intersection mutation differential evolution (IMDE) algorithm, and the JDE self-adaptive algorithm. Findings show that the algorithm produces several solutions obtained by the previously published methods especially for the continuous unimodal function, the quartic function, the multimodal function and the discontinuous step function. In addition, the finding shows that the proposed algorithm can produce optimal solutions efficiently on benchmark instances within short computational time.     

A hybrid genetic algorithm for the traveling salesman problem with pickup and delivery

In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.     

SOLVING THE OPEN SHOP SCHEDULING PROBLEM VIA A HYBRID GENETIC-VARIABLE NEIGHBORHOOD SEARCH ALGORITHM

In this article, a hybrid metaheuristic method for solving the open shop scheduling problem (OSSP) is proposed. The optimization criterion is the minimization of makespan and the solution method consists of four components: a randomized initial population generation, a heuristic solution included in the initial population acquired by a Nawaz-Enscore-Ham (NEH)-based heuristic for the flow shop scheduling problem, and two interconnected metaheuristic algorithms: a variable neighborhood search and a genetic algorithm. To our knowledge, this is the first hybrid application of genetic algorithm (GA) and variable neighborhood search (VNS) for the open shop scheduling problem. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches a high quality solution in short computational times. Moreover, 12 new hard, large-scale open shop benchmark instances are proposed that simulate realistic industrial cases.     

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

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 approach based on hybrid genetic algorithm applied to image denoising problem

An approach based on hybrid genetic algorithm (HGA) is proposed for image denoising. In this problem, a digital image corrupted by a noise level must be recovered without losing important features such as edges, corners and texture. The HGA introduces a combination of genetic algorithm (GA) with image denoising methods. During the evolutionary process, this approach applies some state-of-the-art denoising methods and filtering techniques, respectively, as local search and mutation operators. A set of digital images, commonly used by the scientific community as benchmark, is contaminated by different levels of additive Gaussian noise. Another set composed of some Satellite Aperture Radar (SAR) images, corrupted with a multiplicative speckle noise, is also used during the tests. First, the computational tests evaluate several alternative designs from the proposed HGA. Next, our approach is compared against literature methods on the two mentioned sets of images. The HGA performance is competitive for the majority of the reported results, outperforming several state-of-the-art methods for images with high levels of noise.     

一种求解全局优化问题的新混合遗传算法

把简化的二次插值法融入实数编码遗传算法,构成适于求解全局优化问题的混合遗传算法,该混合算法可以较好解决遗传算法的早熟收敛问题,提高了收敛速度,改善了解的质量,并减少了计算量.由于该混合遗传算法对目标函数的性质没有要求,适合求解大规模问题和工程实际问题.通过对23个标准测试函数的仿真实验,并和已有算法的比较,结果表明本文提出的混合遗传算法是非常有效的.     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

A hybrid metaheuristic approach for the capacitated p-median problem

The capacitated p-median problem (CPMP) seeks to obtain the optimal location of p medians considering distances and capacities for the services to be given by each median. This paper presents an efficient hybrid metaheuristic algorithm by combining a proposed cutting-plane neighborhood structure and a tabu search metaheuristic for the CPMP. In the proposed neighborhood structure to move from the current solution to a neighbor solution, an open median is selected and closed. Then, a linear programming (LP) model is generated by relaxing binary constraints and adding new constraints. The generated LP solution is improved using cutting-plane inequalities. The solution of this strong LP is considered as a new neighbor solution. In order to select an open median to be closed, several strategies are proposed. The neighborhood structure is combined with a tabu search algorithm in the proposed approach. The parameters of the proposed hybrid algorithm are tuned using design of experiments approach. The proposed algorithm is tested on several sets of benchmark instances. The statistical analysis shows efficiency and effectiveness of the hybrid algorithm in comparison with the best approach found in the literature.     

Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm

This paper proposes a hybrid metaheuristic for the minimization of makespan in permutation flow shop scheduling problems. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on a greedy randomized constructive heuristic, a genetic algorithm (GA) for solution evolution, and a variable neighbourhood search (VNS) to improve the population. The hybridization of a GA with VNS, combining the advantages of these two individual components, is the key innovative aspect of the approach. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high-quality solutions in short computational times. Furthermore, it requires very few user-defined parameters, rendering it applicable to real-life flow shop scheduling problems.     

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search–greedy randomized adaptive search procedure (MPNS–GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is suitable for solving very large-scale vehicle routing problems as well as other, more difficult combinatorial optimization problems, within short computational time. It is tested on a set of benchmark instances and produced very satisfactory results. The algorithm is ranked in the fifth place among the 39 most known and effective algorithms in the literature and in the first place among all nature inspired methods that have ever been used for this set of instances.     

A hybrid variable neighborhood search algorithm for solving the limited-buffer permutation flow shop scheduling problem with the makespan criterion

This paper investigates the limited-buffer permutation flow shop scheduling problem (LBPFSP) with the makespan criterion. A hybrid variable neighborhood search (HVNS) algorithm hybridized with the simulated annealing algorithm is used to solve the problem. A method is also developed to decrease the computational effort needed to implement different types of local search approaches used in the HVNS algorithm. Computational results show the higher efficiency of the HVNS algorithm as compared with the state-of-the-art algorithms. In addition, the HVNS algorithm is competitive with the algorithms proposed in the literature for solving the blocking flow shop scheduling problem (i.e., LBPFSP with zero-capacity buffers), and finds 54 new upper bounds for the Taillard's benchmark instances.     

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.     

A hybrid shifting bottleneck-tabu search heuristic for the job shop total weighted tardiness problem

In this paper, we study the job shop scheduling problem with the objective of minimizing the total weighted tardiness. We propose a hybrid shifting bottleneck-tabu search (SB-TS) algorithm by replacing the re-optimization step in the shifting bottleneck (SB) algorithm by a tabu search (TS). In terms of the shifting bottleneck heuristic, the proposed tabu search optimizes the total weighted tardiness for partial schedules in which some machines are currently assumed to have infinite capacity. In the context of tabu search, the shifting bottleneck heuristic features a long-term memory which helps to diversify the local search. We exploit this synergy to develop a state-of-the-art algorithm for the job shop total weighted tardiness problem (JS-TWT). The computational effectiveness of the algorithm is demonstrated on standard benchmark instances from the literature.     

A hybrid evolutionary algorithm for the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) is an NP-hard optimization problem. RCPSP is one of the most important and challenging problems in the project management field. In the past few years, many researches have been proposed for solving the RCPSP. The objective of this problem is to schedule the activities under limited resources so that the project makespan is minimized. This paper proposes a new algorithm for solving RCPSP that combines the concepts of negative selection mechanism of the biologic immune system, simulated annealing algorithm (SA), tabu search algorithm (TS) and genetic algorithm (GA) together. The performance of the proposed algorithm is evaluated and compared to current state-of-the-art metaheuristic algorithms. In this study, the benchmark data sets used in testing the performance of the proposed algorithm are obtained from the project scheduling problem library. The performance is measured in terms of the average percentage deviation from the critical path lower bound. The experimental results show that the proposed algorithm outperforms the state-of-the-art metaheuristic algorithms on all standard benchmark data sets.     

A hybrid evolutionary approach for heterogeneous multiprocessor scheduling

This article investigates the assignment of tasks with interdependencies in a heterogeneous multiprocessor environment; specific to this problem, task execution time varies depending on the nature of the tasks as well as with the processing element assigned. The solution to this heterogeneous multiprocessor scheduling problem involves the optimization of complete task assignments and processing order between the assigned processors to arrive at a minimum makespan, subject to a precedence constraint. To solve an NP-hard combinatorial optimization problem, as is typified by this problem, this paper presents a hybrid evolutionary algorithm that incorporates two local search heuristics, which exploit the intrinsic structure of the solution, as well as through the use of specialized genetic operators to promote exploration of the search space. The effectiveness and contribution of the proposed features are subsequently validated on a set of benchmark problems characterized by different degrees of communication times, task, and processor heterogeneities. Preliminary results from simulations demonstrate the effectiveness of the proposed algorithm in finding useful schedule sets based on the set of new benchmark problems.