Graph partitioning divides a graph into several pieces by cutting edges. The graph partitioning problem is to divide so that the number of vertices in each piece is the same within some defined tolerance and the number of cut edges separating these pieces is minimised. Important examples of the problem arise in partitioning graphs known as meshes for the parallel execution of computational mech...

Water conservancy project scheduling is an extension to the classic resource-constrained problem (RCPSP). It limited by special time constraints called “forbidden windows” during which certain activities cannot be executed. To address this issue, a specific RCPSP model proposed, and approach designated for it incorporates both priority rule-based heuristic algorithm obtain acceptable solution, ...

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO...

The bandwidth-delay-constrained least-cost multicast routing is a challenging problem in high-speed multimedia networks. Computing such a constrained Steiner tree is an NP-complete problem. In this paper, we propose a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA). In the proposed method, the degree-based permutation encoding is used for genotype representation...

Redundancy allocation problems (RAPs) have attracted much attention for the past thirty years due to its wide applications in improving the reliability of various engineering systems. Since RAP is an NP-hard problem and exact methods are only applicable to small instances, various heuristic and meta-heuristic methods have been proposed to solve it. In the literature, most studies on RAPs have b...

-In this paper we glance through the various approaches used by the researchers to develop an automatic timetable using Genetic algorithms. The optimized genetic algorithm can be used with the heuristic approach to design and develop the timetable of an institute. At stake during the process of development, the stakeholders are the professors and the students. The efficient utilization of the i...

Genetic algorithms are random heuristic search (RHS) algorithms with a wide range of applications in adaptation and optimisation problems. The most advanced approach for a general theory of genetic algorithms is offered by the dynamical system model which describes the stochastic trajectory of a population under the dynamics of a genetic algorithm with the help of an underlying deterministic he...

Research in the domain of school timetabling has essentially focused on applying various techniques such as integer programming, constraint satisfaction, simulated annealing, tabu search and genetic algorithms to calculate a solution to the problem. Optimization techniques like simulated annealing, tabu search and genetic algorithms generally explore a solution space. Hyper-heuristics, on the o...

On parallel and distributed heterogeneous computing systems, a heuristic-based task scheduling algorithm typically consists of two phases: task prioritization and processor selection. In a heuristic based task scheduling algorithm, different prioritization will produce different makespan on a heterogeneous computing system. Therefore, a good scheduling algorithm should be able to efficiently as...

The eecient implementation of parallel processing ar-chitectures generally requires the solution of hard combi-natorial problems involving allocation and scheduling of tasks. Such problems can often be modelled with considerable accuracy as a graph partitioning problem|a problem well-known from complexity theory to be NP-hard, which implies that for practical purposes the global optimum will no...