Hybrid Meta-heuristic Method Research Articles

Scheduling of multi-component products in a two-stage flexible flow shop

In this research, the problem of scheduling and sequencing of two-stage assembly-type flexible flow shop with dedicated assembly lines, which produce different products according to requested demand during the planning horizon with the aim of minimizing maximum completion time of products is investigated. The first stage consists of several parallel machines in site I with different speeds in processing components and one machine in site II, and the second stage consists of two dedicated assembly lines. Each product requires several kinds of components with different sizes. Each component has its own structure which leading to difference processing times to assemble. Products composed of only single-process components are assigned to the first assembly line and products composed of at least a two-process component are assigned to the second assembly line. Components are placed on the related dedicated assembly line in the second phase after being completed on the assigned machines in the first phase and final products will be produced by assembling the components. The main contribution of our work is development of a new mathematical model in flexible flow shop scheduling problem and presentation of a new methodology for solving the proposed model. Flexible flow shop problems being an NP-hard problem, therefore we proposed a hybrid meta-heuristic method as a combination of simulated annealing (SA) and imperialist competitive algorithms (ICA). We implement our obtained algorithm and the ones obtained by the LINGO9 software package. Various parameters and operators of the proposed Meta-heuristic algorithm are discussed and calibrated by means of Taguchi statistical technique.

1st International Symposium and 10th Balkan Conference on Operational Research (BALCOR 2011)

Several operations research methods and techniques have been applied to solve a diversity of problems over the decades. Highly interdisciplinary, operations research involves researchers and practitioners across the fields of statistics, mathematics, economics, engineering, computer science, and information technology. This special volume of the Annals of Operations Research is devoted to the 1st International Symposium and 10th Balkan Conference on Operational Research that was held in Thessaloniki, Greece, in September 2011. Therefore, among the papers included there are extended versions of selected papers that were originally presented at that event. The original papers have been revised, extended, and enhanced to reflect current developments, to reflect more recent results of the authors’ work, and to consider new emerging challenges. More specifically, in the first paper, Miroslav Maric, Zorica Stanimirovic, and Srdjan Božovic propose a hybrid metaheuristic method that is used to determine locations for long term health care facilities. The authors explain the current importance of health care facilities due to the aging of the population. The paper deals with the Term Care Facility Location Problem (LTCFLP) which is used to determine the locations of long term facilities in various sites and focusses on how tominimize themaximum number of patients assigned to facilities. An efficient hybrid method is developed that is based on the evolutionary approach (EA) and a modified variable neighborhood search method (VNS). An addition to the algorithm is a local search procedure. Moreover the algorithm has been benchmarked using an established dataset. The second paper, by Roxani Karagiannis, is concerned with a system of equations used to explain the simultaneous effect of contextual variables on technical efficiency and capacity utilization. To evaluate the proposed model, a sample of public Greek hospitals has been used to estimate the capacity utilization and the optimal input. The research proves that there is variation between the capacities in urban and non-urban hospitals. Moreover the results indicate that doctors operated with excess capacity and nursing personnel. Additionally the size of the hospital and its location have a positive effect on the capacity utilization.

A Hybrid Particle-Swarm Tabu Search Algorithm for Solving Job Shop Scheduling Problems

This paper proposes a method for the job shop scheduling problem (JSSP) based on the hybrid metaheuristic method. This method makes use of the merits of an improved particle swarm optimization (PSO) and a tabu search (TS) algorithm. In this work, based on scanning a valuable region thoroughly, a balance strategy is introduced into the PSO for enhancing its exploration ability. Then, the improved PSO could provide diverse and elite initial solutions to the TS for making a better search in the global space. We also present a new local search strategy for obtaining better results in JSSP. A real-integer encode and decode scheme for associating a solution in continuous space to a discrete schedule solution is designed for the improved PSO and the tabu algorithm to directly apply their solutions for intensifying the search of better solutions. Experimental comparisons with several traditional metaheuristic methods demonstrate the effectiveness of the proposed PSO–TS algorithm.

Integrated ANN-HMH Approach for Nonlinear Time-Cost Tradeoff Problem

AbstractThis paper presents an integrated Artificial Neural Network - Hybrid Meta Heuristic(ANN-HMH) method to solve the nonlinear time-cost tradeoff(TCT) problem of real life engineering projects. ANN models help to capture the existing nonlinear time-cost relationship in project activities. ANN models are then integrated with HMH technique to search for optimal TCT profile. HMH is a proven evolutionary multiobjective optimization technique for solving TCT problems. The study has implication in real time monitoring and control of project scheduling processes.

Hybridizing Harmony Search with Biogeography Based Optimization for Global Numerical Optimization

A novel robust hybrid meta-heuristic optimization approach, which can be considered as an improvement of the recently developed biogeography based optimization, namely HSBBO, is proposed to solve global numerical optimization problem. HSBBO combines the exploration of harmony search (HS) with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements and it is demonstrated that, in most situations, the performance of this hybrid meta-heuristic method (HSBBO) is superior to or at least highly competitive with the standard BBO and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, PBIL, PSO and SGA. The effect of the HSBBO parameters is also analyzed. Copyright 2013 American Scientific Publishers.

A hybrid metaheuristic method for the Maximum Diversity Problem

The Maximum Diversity Problem (MDP) consists in selecting a subset of m elements from a given set of n elements (n>m) in such a way that the sum of the pairwise distances between the m chosen elements is maximized. We present a hybrid metaheuristic algorithm (denoted by MAMDP) for MDP. The algorithm uses a dedicated crossover operator to generate new solutions and a constrained neighborhood tabu search procedure for local optimization. MAMDP applies also a distance-and-quality based replacement strategy to maintain population diversity. Extensive evaluations on a large set of 120 benchmark instances show that the proposed approach competes very favorably with the current state-of-art methods for MDP. In particular, it consistently and easily attains all the best known lower bounds and yields improved lower bounds for 6 large MDP instances. The key components of MAMDP are analyzed to shed light on their influence on the performance of the algorithm.

Hybrid metaheuristic method for determining locations for long-term health care facilities

Long-term health care facilities have gained an important role in today’s health care environments, due to the global trend of aging of human population. This paper considers the problem of network design in health-care systems, named the Long-Term Care Facility Location Problem (LTCFLP), which deals with determining locations for long-term care facilities among given potential sites. The objective is to minimize the maximal number of patients assigned to established facilities. We have developed an efficient hybrid method, based on combining the Evolutionary Approach (EA) with modified Variable Neighborhood Search method (VNS). The EA method is used in order to obtain a better initial solution that will enable the VNS to solve the LTCFLP more efficiently. The proposed hybrid algorithm is additionally enhanced by an exchange local search procedure. The algorithm is benchmarked on a data set from the literature with up to 80 potential candidate sites and on large-scale instances with up to 400 nodes. Presented computational results show that the proposed hybrid method quickly reaches all optimal solutions from the literature and in most cases outperforms existing heuristic methods for solving this problem.

A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

Incorporating mutation scheme into krill herd algorithm for global numerical optimization

Recently, Gandomi and Alavi proposed a robust meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization. To improve the performance of the KH algorithm, harmony search (HS) is applied to mutate between krill during the process of krill updating instead of physical diffusion used in KH. A novel hybrid meta-heuristic optimization approach HS/KH is proposed to solve global numerical optimization problem. HS/KH combines the exploration of harmony search (HS) with the exploitation of KH effectively, and hence, it can generate the promising candidate solutions. The detailed implementation procedure for this improved meta-heuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most cases, the performance of this hybrid meta-heuristic method (HS/KH) is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, HS, KH, PSO, and SGA. The effect of the HS/FA parameters is also analyzed.

A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems

ABSTRACT In heterogeneous distributed systems like grid and cloud computing infrastructures, the major problem is the task scheduling which can have much impact on system performance. For some reasons, such as heterogeneous and dynamic features and the dependencies among the requests, this issue is known as a NP-hard problem. In this article a hybrid meta-heuristic method based on Genetic Algorithm (GMSW) is being proposed in order to find a suitable solution for mapping the requests on resources. The proposed method tries to obtain the response quickly, with some goal-oriented operations. It begins, through making a good initial population by merging some features of the Best-Fit and Round Robin methods and a bi-directional tasks prioritization in unbalanced-structured workflow, considering their impact on each other, based on graph topology. Some other operations control and lead the algorithm steps in order to obtain the solution by using efficient parameters in the mentioned systems. Here the focus is on optimizing the makespan and reliability, by considering a good distribution of workload on resources. The experiments here indicate that the GMSW improves the results, with the increasing number of tasks in application graph, for the mentioned objectives. The results are compared with other studied algorithms.

A metaheuristic method for vehicle routing problem based on improved ant colony optimization and Tabu search

A mixed integer programming mathematical formulation of vehicle routing problem with simultaneous pickups and deliveries, and time window constraints (VRP-SPDTW) is presented in this paper. The proposed model aims at minimizing the vehicle number and the overall travel cost. A novel two-stage hybrid metaheuristic method for VRP-SDPTW is also proposed. The first stage adopts improved ant colony optimization (IACO) to determine the minimum number of used vehicles. The second stage employs improved Tabu search to optimize the total travel cost, in which the initial solutions are obtained by IACO in the first stage. Experimental results demonstrate the effectiveness of the proposed metaheuristic method.

A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

High-Level Relay Hybrid Metaheuristic Method for Multi-Depot Vehicle Routing Problem with Time Windows

This paper presents an efficient hybrid metaheuristic solution for multi-depot vehicle routing with time windows (MD-VRPTW). MD-VRPTW involves the routing of a set of vehicles with limited capacity from a set of depots to a set of geographically dispersed customers with known demands and predefined time windows. The present work aims at using a hybrid metaheuristic algorithm in the class of High-Level Relay Hybrid (HRH) which works in three levels and uses an efficient genetic algorithm as the main optimization algorithm and tabu search as an improvement method. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search. An operator deletion- retrieval strategy is executed which shows the efficiency of the inner working of the proposed method. The proposed algorithm is applied to solve the problems of the standard Cordeau's Instances. Results show that proposed approach is quite effective, as it provides solutions that are competitive with the best known in the literature.

Practical Optimization of Large Logistics Through Hybrid Metaheuristic Method

Practical Optimization of Large Logistics Through Hybrid Metaheuristic Method

ACO-H Metaheuristic Combinatorial Optimization Method

ACO-H hybrid metaheuristic method for combinatorial optimization based on two population approaches — the algorithms of ant colony optimization and H-method is proposed. The conditions are obtained that define a convergence in value to the optimal problem solution. The efficiency of the method algorithms is illustrated by the results of computation experiment concerning the solution of a number of the known problems of combinatorial optimization.

Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm

Three-dimension path planning of uninhabited combat air vehicle (UCAV) is a complicated optimal problem, which mainly focuses on optimizing the flight route considering the different types of constrains under complicated combating environments. A new hybrid meta-heuristic ant colony optimization (ACO) and differential evolution (DE) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the pheromone trail of the improved ACO model during the process of ant pheromone updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the three-dimensional mesh while avoiding the threats area and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic ACO. The realization procedure for this hybrid meta-heuristic approach is also presented in detail. In order to make the optimized UCAV path more feasible, the к-trajectory is adopted for smoothing the path. Finally, series experimental comparison results demonstrate that this proposed hybrid meta-heuristic method is more effective and feasible in UCAV three-dimension path planning than the basic ACO model.

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 evolutionary algorithm for the job shop scheduling problem

In this paper, a hybrid metaheuristic method for the job shop scheduling problem is proposed. The optimization criterion is the minimization of makespan and the solution method consists of three components: a Differential Evolution-based algorithm to generate a population of initial solutions, a Variable Neighbourhood Search method and a Genetic Algorithm to improve the population; the latter two are interconnected. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high quality solutions in short computational times using fixed parameter settings.

A hybrid meta-heuristic method for continuous engineering optimization

In this study we present an efficient new hybrid metaheuristic for solving size optimization of truss structures. The proposed ANGEL method combines ant colony optimization (ACO), genetic algorithm (GA) and local search (LS) strategy. In the presented algorithm ACO and GA search alternately and cooperatively in the solution space. The powerful LS algorithm, which is based on the local linearization of the constraint set, is applied to yield a better feasible or less unfeasible solution when ACO or GA obtains a solution. Test examples show that ANGEL can be more efficient and robust than the conventional gradient based deterministic or the traditional population based heuristic methods in solving explicit (implicit) optimization problems. ANGEL produces highly competitive results in significantly shorter run-times than the previously described approaches.

A hybridization of simulated annealing and electromagnetic-like mechanism for job shop problems with machine availability and sequence-dependent setup times to minimize total weighted tardiness

In this paper, we explore job shop problems with two recently popular and realistic assumptions, sequence-dependent setup times and machine availability constraints to actualize the problem. The criterion is a minimization of total weighted tardiness. We establish a simple criterion to integrate machine availability constraints and scheduling decisions simultaneously. We propose a hybrid meta-heuristic to tackle the given problem. This meta-heuristic method, called EMSA, is a combination of two meta-heuristics: (1) Electromagnetic-like mechanism (EM); and (2) simulated annealing (SA). The hybridization is done to overcome some existing drawbacks of each of these two algorithms. To evaluate the proposed hybrid meta-heuristic method, we carry out a benchmark by which the proposed EMSA is compared with some existing algorithms as well as simulated annealing and electromagnetic-like mechanism alone in a fixed given computational time. All the related results and analysis obtained through the benchmark illustrate that our proposed EMSA is very effective and supersedes the foregoing algorithms.