An early exploratory method to avoid local minima in Ant Colony System

Satukitchai, Thanet; Jearanaitanakij, Kietikul

doi:10.1109/ecticon.2015.7206969

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Thus, the traditional ACO cannot be readily applicable to the vertical alignment optimization problem unless the problem is simplified by considering a fixed predefined set of nodes and cost matrix. Several other attempts were also made to improve ACO by introducing local pheromone updating rule; however, the possibility of getting trapped into a local optimum solution persists (Satukitchai & Jearanaitanakij, 2015). In the vertical alignment development problem, the potential intermediate

VPI

locations are not known a priori and needs vertical curve fitting.…”

Section: Introductionmentioning

confidence: 99%

Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development

Sushma

Roy

Maji

2022

Computer aided Civil Eng

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The vertical alignment optimization is about developing a minimum cost curvilinear vertical profile of constrained grade sections and appropriate non‐overlapping vertical curves passing through fixed control points with elevation constraints. Variations in ground profile and discreteness in unit cutting and filling costs make it a non‐convex, noisy, constrained optimization problem with many local minima. Further, the gradient related constraints and vertical curvature are non‐linear. This paper presents an innovative exploring and exploiting ant colony optimization (E&E‐ACO) algorithm with an appropriate point sampling, vertical curve fitting strategies, and an intuitive feasible region identification approach for solving the vertical alignment optimization problem. The E&E‐ACO algorithm extensively explores the feasible search space to generate a set of potential solutions and effectively exploit the space around the potential solutions for developing the optimized vertical alignment. The efficacy of the proposed method is demonstrated using two case studies. In one case study, the optimized solution by the proposed method had a marginally better objective function value and about three times lesser computational time than the solution by the mesh adaptive direct search method. The optimized alignment satisfied the elevation constraints of fixed control points and imitated the manually designed real‐world vertical alignment. The linearly varying exploration and exploitation parameters had better convergence rate than the other tested variations. Further, the proposed method at the end of 1000 iterations yielded about six times better result than the traditional ACO algorithm.

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locations are not known a priori and needs vertical curve fitting.…”

Section: Introductionmentioning

confidence: 99%

Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development

Sushma

Roy

Maji

2022

Computer aided Civil Eng

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“…In each step of solution construction, an ant arriving in node (city) i chooses the next city to move to as a function of the pheromone values and function of the heuristic values on the arcs connecting city i to the cities the ant has not visited yet until all cities have been visited [6]. The most important drawback of ACO for TSP approach is that it can be easily trapped into local optima [7]. One solution to that drawback was presented where it forces ants to expand their search space considering only the distances of all unvisited paths connected to the current city [7].…”

Section: Introductionmentioning

confidence: 99%

“…The most important drawback of ACO for TSP approach is that it can be easily trapped into local optima [7]. One solution to that drawback was presented where it forces ants to expand their search space considering only the distances of all unvisited paths connected to the current city [7]. Takahashi took a different approach and combined ACO and SA algorithms, where elitist ants periodically increase or decrease the quantity of pheromone with the number of tours increment [8].…”

Section: Introductionmentioning

confidence: 99%

Revisiting Traveling Salesman Problem (TSP): Analysis of GA and SA based Solutions

Bethel

Sevil

2021

Int. J. Recent Contrib. Eng. Sci. IT

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The purpose of this study to analyze genetic algorithm (GA) and simulated an-nealing (SA) based approaches applied to well-known Traveling Salesman Prob-lem (TSP). As a NP-Hard problem, the goal of TSP is to find the shortest route possible to travel all the cities, given a set of cities and distances between cities. In order to solve the problem and achieve the optimal solution, all permutations need to be checked, which gets exponentially large as more cities are added. Our aim in this study is to provide comprehensive analysis of TSP solutions based on two methods, GA and SA, in order to find a near optimal solution for TSP. The re-sults of the simulations show that although the SA executed with faster comple-tion times comparing to GA, it took more iterations to find a solution. Additional-ly, GA solutions are significantly more accurate than SA solutions, where GA found a solution in relatively less iterations. The original contribution of this study is that GA based solution as well as SA based solution are developed to perform comprehensive parameter analysis. Further, a quantifiable comparison is provided for the results from each parameter analysis of GA and SA in terms of performance of solving TSP.

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Unified strategy for intensification and diversification balance in ACO metaheuristic

Sagban

Ku-Mahamud

Bakar

2017

2017 8th International Conference on Information Technology (ICIT)

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This intensification and diversification in AntColony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation). The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited either to the management of pheromone memory or to the adaptation of few parameters. This paper introduces the reactive ant colony optimization (RACO) strategy that sticks to the reactive way of automation using memory, diversity indication, and parameterization. The performance of RACO is evaluated on the travelling salesman and quadratic assignment problems from TSPLIB and QAPLIB, respectively. Results based on a comparison of relative percentage deviation revealed the superiority of RACO over other well-known metaheuristics algorithms. The output of this study can improve the quality of solutions as exemplified by RACO.

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An early exploratory method to avoid local minima in Ant Colony System

Cited by 4 publications

References 5 publications

Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development

Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development

Revisiting Traveling Salesman Problem (TSP): Analysis of GA and SA based Solutions

Unified strategy for intensification and diversification balance in ACO metaheuristic

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