Omicron ACO. A New Ant Colony Optimization Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Benjamın Baran ◽  
Osvaldo Gomez
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Population Based ◽  
Analytical Tool ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two well-known TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.

scholarly journals Opposition-Based Ant Colony Optimization Algorithm for the Traveling Salesman Problem

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1650
Author(s):  
Zhaojun Zhang ◽  
Zhaoxiong Xu ◽  
Shengyang Luan ◽  
Xuanyu Li ◽  
Yifei Sun
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Combinatorial Optimization Problems ◽  
Opposition Based Learning ◽  
The Traveling Salesman Problem

Opposition-based learning (OBL) has been widely used to improve many swarm intelligent optimization (SI) algorithms for continuous problems during the past few decades. When the SI optimization algorithms apply OBL to solve discrete problems, the construction and utilization of the opposite solution is the key issue. Ant colony optimization (ACO) generally used to solve combinatorial optimization problems is a kind of classical SI optimization algorithm. Opposition-based ACO which is combined in OBL is proposed to solve the symmetric traveling salesman problem (TSP) in this paper. Two strategies for constructing opposite path by OBL based on solution characteristics of TSP are also proposed. Then, in order to use information of opposite path to improve the performance of ACO, three different strategies, direction, indirection, and random methods, mentioned for pheromone update rules are discussed individually. According to the construction of the inverse solution and the way of using it in pheromone updating, three kinds of improved ant colony algorithms are proposed. To verify the feasibility and effectiveness of strategies, two kinds of ACO algorithms are employed to solve TSP instances. The results demonstrate that the performance of opposition-based ACO is better than that of ACO without OBL.

Unit Commitment by Evolving Ant Colony Optimization

2010 ◽  
Vol 1 (3) ◽  
pp. 67-77 ◽  
Author(s):  
K. Vaisakh ◽  
L. R. Srinivas
Keyword(s):  
Ant Colony Optimization ◽  
Unit Commitment ◽  
Optimization Problems ◽  
Problem Formulation ◽  
Ant Colony ◽  
Spinning Reserve ◽  
Combinatorial Optimization Problems ◽  
Start Up ◽  
The Traveling Salesman Problem ◽  
Optimal Set

Ant Colony Optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem, and multistage decision making of ACO has an edge over other conventional methods. In this paper, the authors propose the Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, start up cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on the systems with number of generating units in the range of 10 to 60. The test results are encouraging and compared with those obtained by other methods.

scholarly journals Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Abdulqader M. Mohsen
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Mutation Operator ◽  
Combinatorial Optimization Problems ◽  
Wide Range

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

Improving Ant Colony Optimization Algorithms for Solving Traveling Salesman Problems

2007 ◽  
Vol 11 (4) ◽  
pp. 433-442 ◽  
Author(s):  
Kuo-Sheng Hung ◽  
◽  
Shun-Feng Su ◽  
Zne-Jung Lee ◽  
◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Search Space ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Computational Time ◽  
Search Performance ◽  
Problem Size ◽  
Traveling Salesman Problems ◽  
Combinatorial Optimization Problems

Ant colony optimization (ACO) has been successfully applied to solve combinatorial optimization problems, but it still has some drawbacks such as stagnation behavior, long computational time, and premature convergence. These drawbacks will be more evident when the problem size increases. In this paper, we reported the analysis of using a lower pheromone trail bound and a dynamic updating rule for the heuristic parameters based on entropy to improve the efficiency of ACO in solving Traveling Salesman Problems (TSPs). TSPs are NP-hard problem. Even though the problem itself is simple, when the number of city is large, the search space will become extremely large and it becomes very difficult to find the optimal solution in a short time. From our experiments, it can be found that the proposed algorithm indeed has superior search performance over traditional ACO algorithms do.

scholarly journals Use of Ant Colony Optimization Algorithm for Determining Traveling Salesman Problem Routes

2019 ◽  
Vol 5 (2) ◽  
pp. 100-111
Author(s):  
Bib Paruhum Silalahi ◽  
Nurul Fathiah ◽  
Prapto Tri Supriyo
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Execution Time ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Food Sources ◽  
Ant Colony Optimization Algorithm ◽  
Initial Node ◽  
The Traveling Salesman Problem

Ant Colony Optimization is one of the meta-heuristic methods used to solve combinatorial optimization problems that are quite difficult. Ant Colony Optimization algorithm is inspired by ant behavior in the real world to build the shortest path between food sources and their nests. Traveling Salesman Problem is a problem in optimization. Traveling Salesman Problem is a problem to find the minimum distance from the initial node to the whole node with each node must be visited exactly once and must return to the initial node. Traveling Salesman Problem is a non-deterministic polynomial-time complete problem. This research discusses the solution of the Traveling Salesman Problem using the Ant Colony Optimization algorithm and also using the exact algorithm. The results showed that the greater the size of the Traveling Salesman Problem case, the longer the execution time required. The results also showed that the execution times of the Ant Colony Optimization are much faster than the execution time of the exact method.

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