Dynamic Vehicle Routing Problem: Solution by Ant Colony Optimization with Hybrid Immigrant Schemes

K.M., Dhanya; Kanmani, S.

doi:10.5815/ijisa.2017.07.06

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Cloud computing is an on-demand resource for networks, servers, programs and services with high speed and little effort in interaction of users. One of the recent challenges in a computing platform is TS [3,18]. The general issue in TS is NP-hard optimization problems like travelling salesman problem and combinatorial problems like integer programming and addressing problems.…”

Section: Introductionmentioning

confidence: 99%

MACO-MOTS: Modified Ant Colony Optimization for Multi Objective Task Scheduling in Cloud Environment

Reddy¹,

Kumar²

2019

IJISA

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Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts. One of the main issues in a cloud computing environment is Task scheduling (TS). In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm. The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency. MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature.

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Section: Introductionmentioning

confidence: 99%