Modified A* Algorithm integrated with ant colony optimization for multi-objective route-finding; case study: Yazd

2021 ◽  
Vol 113 ◽  
pp. 107877
Author(s):  
Leila Pasandi ◽  
Mehrnaz Hooshmand ◽  
Morteza Rahbar
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
A Algorithm ◽  
Multi Objective ◽  
Route Finding

scholarly journals A hybrid technique for single-source shortest path-based on A* algorithm and ant colony optimization

2020 ◽  
Vol 9 (2) ◽  
pp. 356
Author(s):  
Sameer Alani ◽  
Atheer Baseel ◽  
Mustafa Maad Hamdi ◽  
Sami Abduljabbar Rashid
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Paths ◽  
Ant Colony ◽  
Fast Time ◽  
Hybrid Technique ◽  
Single Source ◽  
A Algorithm ◽  
A New Technique

<span lang="EN-US">In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in fast time processing. ACO will optimize this path and output the best path. The result showed that the proposed solution provides an average decreasing time performance is 13.5%.</span>

A multi-objective disassembly planning approach with ant colony optimization algorithm

2008 ◽  
Vol 222 (11) ◽  
pp. 1465-1474 ◽  
Author(s):  
C Lu ◽  
H Z Huang ◽  
J Y H Fuh ◽  
Y S Wong
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Uniform Design ◽  
Pareto Frontier ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Multi Objective ◽  
Planning Approach ◽  
Disassembly Planning

This paper proposes a multi-objective disassembly planning approach with an ant colony optimization algorithm. The mechanism of ant colony optimization in disassembly planning is discussed, and the objectives to be optimized in disassembly planning are analysed. In order to allow a more effective search for feasible non-dominated solutions, a multi-objective searching algorithm with uniform design is investigated to guide the ants searching the routes along the uniformly scattered directions towards the Pareto frontier; based on the above searching algorithm, an ant colony optimization algorithm for disassembly planning is developed. The results of a case study are given to verify the proposed approach.

scholarly journals Efficient multi-objective optimization methods for computationally intensive urban water resources models

2014 ◽  
Vol 17 (1) ◽  
pp. 36-55 ◽  
Author(s):  
Mohammad Mortazavi-Naeini ◽  
George Kuczera ◽  
Lijie Cui
Keyword(s):  
Ant Colony Optimization ◽  
Water Resource ◽  
Optimization Methods ◽  
Optimization Method ◽  
Ant Colony ◽  
Urban Water ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multi-objective optimization methods require many thousands of objective function evaluations. For urban water resource problems such evaluations can be computationally very expensive. The question as to which optimization method is the best choice for a given function evaluations budget in urban water resource problems remains unexplored. The main objective of this paper is to address this question. The second objective is to develop a new optimization algorithm, efficient multi-objective ant colony optimization-I (EMOACO-I), which exploits the good performance of ant colony optimization enhanced using ideas borrowed from evolutionary optimization. Its performance was compared against three established methods (NSGA-II, SMPSO, εMOEA) using two case studies based on the urban water resource systems serving two major Australian cities. The case study problems involved two or three objectives and 10 or 13 decision variables affecting infrastructure investment and system operation. The results show that NSGA-II was the worst performing method. However, none of the remaining methods was unambiguously superior. For example, while EMOACO-I converged more rapidly, its diversity was comparable but not superior to the other methods. Greater differences in performance were found as the number of objectives and case study complexity increased. This suggests that pooling the results from a number of methods could help guard against the vagaries in performance of individual methods.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

2016 ◽  
Vol 5 (4) ◽  
pp. 165-191 ◽  
Author(s):  
Boominathan Perumal ◽  
Aramudhan M.
Keyword(s):  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Bin Packing ◽  
Virtual Machines ◽  
Transition Probability ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Placement Problem ◽  
Multi Objective

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

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