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Performance Measurement of Energy Optimal Path Finding for Waste Collection Robot Using ACO Algorithm
(2022-01-01)
Tomitagawa, Koki
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Anuntachai, Anuntapat
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Chotipant, Supannada
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Wongwirat, Olarn
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Kuchii, Shigeru
Indoor waste collection that utilizes mobile robots can solve the labor cost and manpower shortage but has the problem of limited energy resources, making it difficult to operate for long periods of time. Therefore, it is important to reduce the energy consumption for efficient waste collection. The waste collection robot can be modeled as a Capacitated Vehicle Routing Problem (CVRP), where heuristics algorithms can be deployed to search for the most energy-efficient path. This paper proposes the Ant Colony Optimization (ACO) algorithm for finding the optimal path of the waste collection robot. Energy consumption of the robot depends not only on the travel path but also on the weight of the waste it carries. Therefore, the proposed ACO algorithm utilizes the path distance and waste weight as the visibility. The travel distance and energy consumption are also used to determine the updated pheromone. Whereas the conventional and adapted ACO algorithms use only either the path distance or the waste weight as the visibility, respectively. The simulation experiments are conducted to compare the travel distance and the energy consumption that the waste collection robot takes by using the conventional, adapted, and proposed ACO algorithms. In the simulation experiments, the number of nodes, the waste weight, and the carrying capacity are used as parameters to verify the performance under the determined environment. The simulation results express that the proposed ACO algorithm provides a better energy optimal path in terms of travel distance and energy consumption than the conventional and adapted ACO algorithms.
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A heuristic approach for scheduling in heterogeneous distributed embedded systems
(2020-02-01)
Prongnuch, Sethakarn
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Sitjongsataporn, Suchada
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Wiangtong, Theerayod
This paper presents a heuristic approach for workflow scheduling in heterogeneous distributed embedded system (HDES). A genetic algorithm (GA) and ant colony optimization (ACO) modified with the greedy algorithm introduced to the system contains multiple heterogeneous embedded machines (HEMs) working as a cluster. Users can remotely access and utilize their computational power. The communications on different types of buses are taken into account to find an optimal solution. New meta-heuristic information based on forwarding dependency is proposed to build probability for ACO to generate task priorities. Besides, a greedy algorithm for machine allocation is incorporated to complete task scheduling. Experiments based on random task graphs running in the HEM cluster demonstrate the effectiveness of the modified greedy ant colony optimization algorithm which outperforms the others by 33% more result quality.
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An ant colony optimization method for the capacitated vehicle routing problem with stochastic demands
(2017-02-21)
Janjarassuk, Udom
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Masuchun, Ruedee
The ant colony optimization (ACO) method has been extensively studied recently for solving many combinatorial optimization problems. In this paper, an ant colony optimization method for solving the vehicle routing problem with stochastic demands is presented. The 2-opt local search is employed within the ACO algorithm to improve the qualities of the solutions. Simulation technique is used for the estimate of the expected cost under stochastic demands. Computational results are also reported.
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A comparison of heuristic approaches to the travelling salesman problem
(2011-02-01)
Janjarassuk, Üdom
In this paper, we present three heuristic methods for solving the traveling salesman problem. Implementations of the nearest neighbor algorithm, the 2-opt heuristic, and the ant colony optimization (ACO) method is tested by using instances from the TSPLIB. Computational results are compared in terms of solution quality and computational efficiency. We also provide results from the variants of the 2-opt heuristic and the ACO method by utilizing an initial tour constructed by the nearest neighbor algorithm. ICIC International ©2011 ISSN.
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Reliability optimization of topology communication network design using an improved ant colony optimization
(2009-09-01)
Watcharasitthiwat, Kanyapat
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Wardkein, Paramote
Network design problem is a well-known NP-hard problem which involves the selection of a subset of possible links or a network topology in order to minimize the network cost subjected to the reliability constraint. To overcome the problem, this paper proposes a new efficiency algorithm based on the conventional ant colony optimization (ACO) to solve the communication network design when considering both economics and reliability. The proposed method is called improved ant colony optimizations (IACO) which introduces two addition techniques in order to improve the search process, i.e. neighborhood search and re-initialization process. To show its efficiency, IACO is applied to test with three different topology network systems and its results are compared with those obtained results from the conventional approaches, i.e. genetic algorithm (GA), tabu search algorithm (TSA) and ACO. Simulation results, obtained these test problems with various constraints, shown that the proposed approach is superior to the conventional algorithms both solution quality and computational time. © 2009 Elsevier Ltd. All rights reserved.
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Topological communication network design using ant colony optimization
(2005-01-01)
Premprayoon, Patomporn
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Wardkein, Paramote
In this paper, Ant Colony Optimization (ACO) is proposed to solve the optimal telecommunication networks design considering both economics and reliability. ACQ is a new cooperative agent approach, which is inspired by the observation or the behaviors of real ant colonies on the topic of ant trial formation and foraging methods. In the ACO, a set of cooperating agents called "ants" cooperates to find good solution. The advantages of ACO are parallel search and optimization capabilities. This approach is interested on the model of the reality telecommunication network. Simulation results on multiple test problems with various degrees of constraint show that this approach has high efficiency and is superior to the conventional Tabu Search algorithm (TSA).

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