Improvement Heuristic Algorithm Research Articles

An Inclined Scalable Grid System and an Improved Heuristic Algorithm for Multisatellite Joint Regional Coverage

An Inclined Scalable Grid System and an Improved Heuristic Algorithm for Multisatellite Joint Regional Coverage

On Cost-Aware Heterogeneous Cloudlet Deployment for Mobile Edge Computing

Edge computing undertakes downlink cloud services and uplink terminal computing tasks, data interaction latency and network transmission cost are thus significantly reduced. Although a lot of research has been conducted in mobile edge computing (MEC), which assumed that all homogeneous cloudlets are placed in WMAN and user mobility is also ignored, little attention is paid to how to place heterogeneous cloudlets in wireless metropolitan area network (WMAN) to minimize the deployment cost of cloudlets. Meanwhile, the method of selecting an optimal access point (AP) for deployment, modeling and heuristic algorithm (HA) needs to be improved. Therefore, this paper design a new heterogeneous cloudlet deployment model considering the quality of service (QoS) and mobility of users, and the Improved Heuristic Algorithm (IHA) is proposed to minimize cloudlet deployment cost. The extensive simulations demonstrate that IHA is more efficient than HA and the designed model is superior to the existing work.

Population-based improvement heuristic with local search for single-row facility layout problem

The Single-Row Facility Layout Problem (SRFLP) is a well-known combinatorial optimization problem. The objective of SRFLP is to find out the arrangement of facilities with given lengths on a line so that the weighted sum of the distances between all pairs of facilities is minimized. This problem is known to be NP-hard. Hence, a population-based improvement heuristic algorithm with local search is presented in this article to solve SRFLP. The proposed algorithm works well also for the Single-Row Equidistant Facility Layout Problem (SREFLP), where the length of each facility is equal. The computational efficiency of the proposed algorithm is checked with the instances of sizes ranging from 5 to 300 available in the literature for SRFLP and SREFLP. The obtained results are compared to those from different state-of-the-art algorithms. The proposed algorithm achieves best known solutions to date for every instance considered in this article in reasonable computational time.

An improvement heuristic for permutation flow shop scheduling

An improvement heuristic algorithm is proposed in this paper for solving flow shop scheduling problem (Fm / prmu / Cmax). To test its efficiency, firstly the performance of the proposed algorithm is done against the six heuristics existing in the literature including the best NEH heuristic on 120 Taillard benchmarks. Further, this set of 120 Taillard instances is increased to 266 benchmark problem instances which include Carlier's, some Reeves and some new hard VRF instances from Vallada et al. (2015). On these instances, the performance of the proposed algorithm is tested against the best NEH and the famous CDS heuristic with the best known upper bounds. Further, a brief analysis of the other heuristics and metaheuristics existing in literature is done on Taillard problem instances. The proposed heuristic outperforms all the heuristics reported in this paper.

An improvement heuristic for permutation flow shop scheduling

An improvement heuristic algorithm is proposed in this paper for solving flow shop scheduling problem (Fm / prmu / Cmax). To test its efficiency, firstly the performance of the proposed algorithm is done against the six heuristics existing in the literature including the best NEH heuristic on 120 Taillard benchmarks. Further, this set of 120 Taillard instances is increased to 266 benchmark problem instances which include Carlier's, some Reeves and some new hard VRF instances from Vallada et al. (2015). On these instances, the performance of the proposed algorithm is tested against the best NEH and the famous CDS heuristic with the best known upper bounds. Further, a brief analysis of the other heuristics and metaheuristics existing in literature is done on Taillard problem instances. The proposed heuristic outperforms all the heuristics reported in this paper.

Solving the Multi-Objective Travelling Salesman Problem with Real Data Application

The aim of this paper is building a mathematical model for Travelling salesman problem (TSP) with multi-objective; the model describes the problem of (TSP) with three objectives (cost, distance, time), Real data were collected with a sample of twenty states of United State of America, Three methods were used (Branch and Bound algorithm, Nearest neighbor and two-way exchange improvement heuristic), The comparison was conducted among results reached. To solve the problem multi-objective of (TSP), The weighted model demonstrated the effectiveness and flexibility to solve real problems of multi-objective (TSP), where it can be said that it is impossible to solve this problem without resorting to multiple -objective mathematical models, In other words, the number of possible rout for the 20 town is , to find the optimal routs among these routs it takes very long time and a lot of effort, here stand out importance of two-way exchange improvement heuristic algorithm, where this rout is satisfactory to the decision maker in terms of cost, distance and time.

An Improved Heuristic Algorithm for UCAV Path Planning

The study of unmanned combat aerial vehicle (UCAV) path planning is increasingly important in military and civil field. This paper presents a new mathematical model and an improved heuristic algorithm based on Sparse A⁎ Search (SAS) for UCAV path planning problem. In this paper, flight constrained conditions will be considered to meet the flight restrictions and task demands. With three simulations, the impacts of the model on the algorithms will be investigated, and the effectiveness and the advantages of the model and algorithm will be validated.

The Application of IHA in Grid Cloud Computing Task Decomposition and Scheduling Based on Bionics

Based on the large amount and variations of the power grid task as well as its requirement of real- time performance and economic benefit, we make a further improvement and expansion of IHA (Improved Heuristic Algorithm) on the combination of bionics in genetic engineering and evolution to solve the decomposing and scheduling problems. Firstly, we transform those complex decomposing problems into the operational optimal solution problem by IHA to decrease the rate of running into the local optimal solution [1]. In task scheduling, we classify the sub-tasks by the emergency degree for resource allocation, which not only largely reduces the calculation and resource cost but also improves working efficiency and the speed of execution [2]. Finally, we select optimal scheduling scheme by the Fitness function defined about time and cost.

An Improved Heuristic Algorithm for a Hybrid Flow-Shop Scheduling

This paper presents an improved metaheuristic algorithm to minimize the makespan in a hybrid flow-shop scheduling (HFS) with non-identical parallel machines. First, a mathematical model for an HFS problem is introduced. Second, an improved simulated annealing algorithm (ISAA) which is inspired from a hormone modulation mechanism is presented to retrofit speed and accuracy of the algorithm. Finally, the computer simulation demonstrates the good quality of the proposed procedure, and it outperforms several other algorithms.

An Improved Heuristic Algorithm for Finding Motif Signals in DNA Sequences

The planted (l, d)-motif search problem is a mathematical abstraction of the DNA functional site discovery task. In this paper, we propose a heuristic algorithm that can find planted (l, d)-signals in a given set of DNA sequences. Evaluations on simulated data sets demonstrate that the proposed algorithm outperforms current widely used motif finding algorithms. We also report the results of experiments on real biological data sets.

Optimizing landscape configuration: A case study of woodland birds in the Mount Lofty Ranges, South Australia

We formulate the optimal landscape reconstruction problem for 22 birds in the Mount Lofty Ranges (MLR), South Australia. The goal is to determine landscape configurations with revegetation that would maximize the projected number of bird species present across all revegetated sites in the landscape. We use simulated annealing and an iterative improvement heuristic algorithm to find the efficient solutions for different objective functions and budget sizes. Under scenarios assuming that possible sites for revegetation have equal costs, our analyses suggest that revegetation programs in the region should strive to create landscapes with a mean revegetation patch size ranging from 780 to 4010 ha. The inclusion of property value data as surrogates for revegetation costs results in optimal landscapes with more highly irreplaceable (priority) sites in less expensive parts of the region and smaller average patch sizes. This illustrates how the solutions to landscape design problems change with different assumptions of economic cost. The paper represents one of the first uses of decision-modeling tools for optimal habitat restoration on a real landscape. The software and methodology have applicability for general landscape design outside the Mount Lofty Ranges.

Constructive and improvement flow shop scheduling heuristics: An extensive evaluation

A number of optimization techniques are available to solve the scheduling problems. Most of the scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain 'good' solutions in reasonable time. Scheduling deals with the allocation of resources. Scheduling is a decision-making process that has, as a goal, the optimization of two or more objectives. The specific goal of this paper is to perform a comparative evaluation of the exisiting constructive heuristic algorithms and to propose an improvement heuristic algorithm. The improvement heuristic algorithm proposed is a genetic algorithm. The benchmark problems are used to evaluate these algorithms. The genetic algorithm (GA) is a potentially powerful tool for solving complex optimization problems. In this paper, the heuristic procedures considered for evaluation are Palmer's algorithm, Gupta's algorithm, C D S algorithm, Dannenbring's R A algorithm, N E H algorithm and the proposed genetic algorithm. The algorithms are coded in G language. The performance measure considered for evaluation is the makespan.

A joint determination of storage locations and space requirements for correlated items in a miniload automated storage-retrieval system

Abstract We study the problem of clustering inventory items to assigned storage locations. Inventory-related cost as well as material-handling cost is considered to determine the space requirement and the storage location of each item simultaneously. An improvement heuristic algorithm is developed for the problem. We provide a numerical example to illustrate the algorithm developed. The performance of the algorithm is evaluated through numerical experimentation.