Travelling Salesman Problem Optimization Using Genetic Algorithm

Juneja, Sahib Singh; Saraswat, Pavi; Singh, Kumud K.; Sharma, Jatin; Majumdar, Rana; Chowdhary, Sunil Kumar

doi:10.1109/aicai.2019.8701246

Cited by 39 publications

(15 citation statements)

References 4 publications

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“…Hai et al [16] used GA to improve the performance of an internal combustion engine. Besides, Juneja et al [17] utilized GA to solve Travelling Salesman Problem. However, all the reviewed literatures were only focused either on servo or regulatory controls.…”

Section: B Literature Studymentioning

confidence: 99%

An empirical Study Investigation of Task Allocation Process Barriers in the Context of Offshore Software Development Outsourcing: An Organization Size Based Analysis

al.¹

2020

IJCDS

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This research presented a holistic approach in determining the trade-off optimized Proportional-Integral-Derivative (PID) tunings for both servo and regulatory controls of the cascade control loop by using Genetic Algorithm (GA). Performance of GA-based PID tunings was significantly compared with the IMC-based single loop tunings and conventional cascade control tunings. GA-based PID tunings eliminated the complicated mathematic calculations in obtaining the correlation PID tuning values and also reduce the dependency on engineering knowledge, experience, and skills. The performance of transient and steady-state responses was compared through time domain specification, performance index, and process response curve. It is concluded that the GA-based PID tunings for the cascade control loop had produced the best result for both servo and regulatory control objectives, which is eventually determined.

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Section: B Literature Studymentioning

confidence: 99%

An empirical Study Investigation of Task Allocation Process Barriers in the Context of Offshore Software Development Outsourcing: An Organization Size Based Analysis

al.¹

2020

IJCDS

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“…Among the different approaches proposed in literature, some of the most relevant are: Genetic Algorithms (GA), Ant Colony Optimisation (ACO), Tabu Search (TS), Adaptive Large Neighbourhood Search (ALNS), Simulating Annealing (SA), Local Search (LS) and Iterated Local Search (ILS). The approach proposed by Juneja et al [8] exploits the ability of population-based heuristics to search for multiple solutions in each iteration of the algorithm and, by using various combinations of selection, crossover and mutation techniques, to continuously improve the quality of current solutions. Dorigo et al [9] were the first to introduce the possibility to use ACO, a heuristic algorithm which navigates the solution space by mimicking ants finding food as a group, as a viable strategy to solve the TSP.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Iterated Local Search with Random Restarts for the Balanced Travelling Salesman Problem

Pierotti

Ferretti

Pozzi

et al. 2021

Advances in Intelligent Systems and Computing

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Metaheuristics have been widely used to solve NP-hard problems, with excellent results. Among all NP-hard problems, the Travelling Salesman Problem (TSP) is potentially the most studied one. In this work, a variation of the TSP is considered; the main differences being, edges may have positive or negative costs and the objective is to return a Hamiltonian cycle with cost as close as possible to zero. This variation is called the balanced TSP (BTSP). To tackle this new problem, we present an adaptive variant of the iterated local search metaheuristic featuring also random restart. This algorithm was tested on the MESS2018 metaheuristic competition and achieved notable results, scoring the 5th position overall. In this paper, we detail all the components of the algorithm itself and present the best solutions identified. Even though this metaheuristic was tailored for the BTSP, with small modifications its structure can be applied to virtually any NP-hard problem. In particular, we introduce the uneven reward-and-punishment rule which is a powerful tool, applicable in many contexts where fast responses to dynamic changes are crucial.

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“…Genetic algorithms are computational methods for selecting solutions that match criteria without having to try out all possible solutions. Genetic Algorithms can be applied in optimizing scheduling, route selection, and placement (Agrawal, 2017;Bayer, 2018;Ellili et al, 2017;Juneja et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Donor Collectors Route Optimisation using Genetic Algorithm Method

Priyandoko

Arofah

Sahar

et al. 2020

IJEEPSE

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Searching for a route to take donation in terms of time and the shortest route is a must for the management because it can save time and effort. In order to make donations more efficient, a system is needed to provide recommendations for taking donation routes, one of which is using a Genetic Algorithm (GA) method. The GA can be applied in optimizing schedules, routes, and spaces. The results show that after testing two different routes with different maximum generation values of 50, 100, 500 and 1000, a maximum generation value of 50 can give optimal results.

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Travelling Salesman Problem Optimization Using Genetic Algorithm

Cited by 39 publications

References 4 publications

An empirical Study Investigation of Task Allocation Process Barriers in the Context of Offshore Software Development Outsourcing: An Organization Size Based Analysis

An empirical Study Investigation of Task Allocation Process Barriers in the Context of Offshore Software Development Outsourcing: An Organization Size Based Analysis

Adaptive Iterated Local Search with Random Restarts for the Balanced Travelling Salesman Problem

Donor Collectors Route Optimisation using Genetic Algorithm Method

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