Genetic Variable Neighborhood Search Research Articles

Hybrid and approximate algorithms for the dial-a-ride problem with adaptive ride times considering different service strategies

The dial-a-ride problem (DARP) is that of satisfying a number of pickup and drop-off customer requests, using a fleet of vehicles. In real life, traffic conditions can affect expected travel times, and hence the satisfaction of rigid pickup/drop-off time windows and scheduled ride times. This article considers the DARP with customer-adaptive ride times under different service conditions and strategies. The problem is formulated as a mixed integer linear programming model that minimizes service cost and customer inconvenience. A hybrid genetic–variable neighbourhood search algorithm (GAVNS) is proposed to solve the problem. Experiments are conducted to compare the performance of GAVNS with that of an approximate column generation algorithm, and to evaluate the application of different strategies on service efficiency and customer satisfaction. The results demonstrate the efficiency of GAVNS, and that limited violations of time-window and ride-time constraints can improve the service efficiency while preserving customer satisfaction levels.

Reliability-Based Heuristic-Based Algorithm for Task Execution in Multiprocessor Systems

Objective: This study implements a combination of heuristicbased algorithm and finding variable neighborhoods, thereby reducing make-span and improving reliability. Methods/statistical analysis: The bi-objective algorithm is proposed for a static planning strategy to achieve high performance in heterogeneous multiprocessor systems. The reliability of a system is based on the probability in which resources of the system execute tasks without any failure. Findings: Here, a genetic algorithm integrated using single neighborhood structure Genetic Variable Neighbourhood Search (GVNS) is implemented to improve the efficient search quality. Applications and improvements: Simulation is performed to maintain better performance parameters when compared with conventional algorithms. Keywords: Directed Acyclic Graph, Inter Process Communication, Evolutionary Algorithm.

Genetic-variable neighborhood search with thread replication for mobile cloud computing

The resource-intensive resettlement procedure and inherent restrictions of the wireless medium obstruct the understanding of faultless implementation in mobile cloud computing (MCC) surroundings. In MCC, transfer of thread execution to high end servers allows the processing of those jobs which demand more resources on the mobile equipment. Hence, implementing the application with stumpy cost, least overhead and non-obtrusive relocation is a demanding research area in MCC. Many scheduling mechanisms have been proposed so far to balance the load between the given set of mobile servers, but it is found to be NP-Hard problem. Therefore, evolutionary techniques are required to balance the load among mobile servers. Genetic Algorithm (GA) has been verified to be fine by jumble up the key values, but it becomes unsuccessful to strengthen the exploration in the rising areas. In Variable Neighborhood Search (VNS), local exploration technique is implemented continually to evaluate optimistic outcomes in neighborhood to attain local best solution. But VNS is still prone to premature convergence traps only because of limited search capability. Therefore hybridization with non-global finding techniques may conquer the limitations and guide the dominant search mechanisms to some extent. The GA along with VNS using thread replication (GVNSTR) is implemented to set stability of non-local searching and local utilization for an evolutionary processing period and get the optimized solution.

Modelling JIT supply chains by simulation and hybrid genetic variable neighbourhood search algorithm

The objective of this study is investigating multi-stage supply chain systems which is controlled using a kanban system so as to analyse and optimise it. In this paper, a new optimisation approach based on simulation analysis and metaheuristic methods to optimise a multi-echelon supply chain in a JIT production context is proposed. A hybrid of simulation modelling, genetic algorithm (GA), and variable neighbourhood search (VNS) to solve the above mentioned problem was proposed. In this research paper, GA-VNS is used iteratively to optimise the supply chain. Furthermore, the performance of the GA and VNS compared with their GA-VNS counterpart based on their relative error, and it is illustrated that the GA-VNS has ability to solve NP-hard problems in the area of complicated simulation optimisation models, especially where there is no prior knowledge of the behaviour of the system.

A hybrid genetic-variable neighborhood search algorithm for the cell formation problem based on grouping efficacy

Cell formation problem attempts to group machines and part families in dedicated manufacturing cells such that the number of voids and exceptional elements in cells are minimized. In this paper, we...

A hybrid genetic-variable neighborhood search algorithm for the cell formation problem based on grouping efficacy

Cell formation problem attempts to group machines and part families in dedicated manufacturing cells such that the number of voids and exceptional elements in cells are minimized. In this paper, we presented a linear fractional programming model with the objective of maximizing the grouping efficacy while the number of cells is unknown. To show the effectiveness of the proposed model, two test problems were applied. Then, to solve the model for real-sized applications, a hybrid meta-heuristic algorithm in which genetic algorithm and variable neighborhood search are combined. Using the grouping efficacy measure, we have also compared the performance of the proposed algorithm on a set of 35 test problems from the literature. The results show that the proposed GA-VNS method outperforms the state-of-the-art algorithms.

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

Effective task scheduling, which is essential for achieving high performance in a heterogeneous multiprocessor system, remains a challenging problem despite extensive studies. In this article, a heuristic-based hybrid genetic-variable neighborhood search algorithm is proposed for the minimization of makespan in the heterogeneous multiprocessor scheduling problem. The proposed algorithm distinguishes itself from many existing genetic algorithm (GA) approaches in three aspects. First, it incorporates GA with the variable neighborhood search (VNS) algorithm, a local search metaheuristic, to exploit the intrinsic structure of the solutions for guiding the exploration process of GA. Second, two novel neighborhood structures are proposed, in which problem-specific knowledge concerned with load balancing and communication reduction is utilized respectively, to improve both the search quality and efficiency of VNS. Third, the proposed algorithm restricts the use of GA to evolve the task-processor mapping solutions, while taking advantage of an upward-ranking heuristic mostly used by traditional list scheduling approaches to determine the task sequence assignment in each processor. Empirical results on benchmark task graphs of several well-known parallel applications, which have been validated by the use of non-parametric statistical tests, show that the proposed algorithm significantly outperforms several related algorithms in terms of the schedule quality. Further experiments are carried out to reveal that the proposed algorithm is able to maintain high performance within a wide range of parameter settings.