Prasanta Dutta
Prasanta Dutta has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Indadul Khan
Department of Computer Science, Chandrakon Vidyasagar Mahavidyalaya, Paschim-Medinipur, India
6 shared publications - Manas Kumar Maiti
Department of Mathematics, Mahishadal Raj College, Mahishadal, Purba-Medinipur, India
6 shared publications - Krishnendu Basuli
Department of Computer Science, West Bengal State University, Kolkata, India
5 shared publications - Sudip Kumar Mandal
Department of Computer Science, Panskura Banamali College, Purba Medinipur, India
2 shared publications - Biswajit Mondal
Raja N.L.K. Womens College
1 shared publication - Kazi Abdul Halim
Department of Mathematics, Panskura Banamali College (Autonomous), Panskura R. S., Purba-Medinipur, India
1 shared publication
Latest Publications
- 3 June 2026
- Published by Springer Nature
- p. 131-143
A Novel Hyperheuristic for the GTSPs Involving Precise and Imprecise Costs
- 3 June 2026
- Published by Springer Nature
- p. 131-143
A Novel Hyperheuristic for the GTSPs Involving Precise and Imprecise Costs
- 8 September 2025
- Published by Springer Nature in OPSEARCH
- p. 1-35
A collaborative use of some perturbation rules for the TSPs in different fields: an application to urban health care
- 8 September 2025
- Published by Springer Nature in OPSEARCH
- p. 1-35
A collaborative use of some perturbation rules for the TSPs in different fields: an application to urban health care
- 17 February 2025
- Published by World Scientific Pub Co Pte Ltd in New Mathematics and Natural Computation
- Vol. 22 (03) , 863-892
A Novel Heuristic for the Generalized Traveling Salesman Problems with Imprecise Cost Matrices
A meta-heuristic approach with multiple perturbation rules is proposed to solve the generalized traveling salesman problems in different environments. The proposed approach consists of two phases. The first phase is devoted for the sequencing of the groups and in the second phase one node is selected from each group to minimize the tour cost. In the first stage a group sequence is generated randomly. After that a [ ]-bit shuffling approach is used to regenerate the new group sequences. In the second phase, for every group sequence several selection rules are used for selecting the nodes/cities from different groups of a particular group sequence. At a regular interval, [ ]-opt operation is used to update the paths and the corresponding group sequences are updated according to the group elements. The proposed algorithm is capable of solving the problem in crisp as well as different imprecise environments, like fuzzy environment, rough environment, etc. Proper decision making approaches are outlined for decision-making in fuzzy and rough environments. Simulation approaches in fuzzy and rough environments are also presented to deal with the problem for different types of fuzzy and rough estimations. In the crisp environment, the algorithm has been tested using different sizes benchmark test instances from GTSPLIB having sizes upto 226 nodes with 26 groups. There are no benchmark data sets available for the GTSPs in imprecise environments. Test problems in imprecise environments are generated from different benchmark crisp test problems of the GTSPLIB and are used to test the algorithm in the corresponding imprecise environments. It is observed from the numerical illustration and graphical representation that the proposed approach obtains 100% accurate results in all the runs of the algorithm for different seeds of the random number generator for all the considered benchmark test problems under consideration. Another observation is visualized from the table that the algorithm has found multiple optimal paths for some test instances. It can be concluded from the observations that the proposed algorithm is capable of solving the GTSPs in crisp as well as in imprecise environments for a significantly large size problems.
- 17 February 2025
- Published by World Scientific Pub Co Pte Ltd in New Mathematics and Natural Computation
- Vol. 22 (03) , 863-892
A Novel Heuristic for the Generalized Traveling Salesman Problems with Imprecise Cost Matrices
A meta-heuristic approach with multiple perturbation rules is proposed to solve the generalized traveling salesman problems in different environments. The proposed approach consists of two phases. The first phase is devoted for the sequencing of the groups and in the second phase one node is selected from each group to minimize the tour cost. In the first stage a group sequence is generated randomly. After that a [ ]-bit shuffling approach is used to regenerate the new group sequences. In the second phase, for every group sequence several selection rules are used for selecting the nodes/cities from different groups of a particular group sequence. At a regular interval, [ ]-opt operation is used to update the paths and the corresponding group sequences are updated according to the group elements. The proposed algorithm is capable of solving the problem in crisp as well as different imprecise environments, like fuzzy environment, rough environment, etc. Proper decision making approaches are outlined for decision-making in fuzzy and rough environments. Simulation approaches in fuzzy and rough environments are also presented to deal with the problem for different types of fuzzy and rough estimations. In the crisp environment, the algorithm has been tested using different sizes benchmark test instances from GTSPLIB having sizes upto 226 nodes with 26 groups. There are no benchmark data sets available for the GTSPs in imprecise environments. Test problems in imprecise environments are generated from different benchmark crisp test problems of the GTSPLIB and are used to test the algorithm in the corresponding imprecise environments. It is observed from the numerical illustration and graphical representation that the proposed approach obtains 100% accurate results in all the runs of the algorithm for different seeds of the random number generator for all the considered benchmark test problems under consideration. Another observation is visualized from the table that the algorithm has found multiple optimal paths for some test instances. It can be concluded from the observations that the proposed algorithm is capable of solving the GTSPs in crisp as well as in imprecise environments for a significantly large size problems.
- Open Access
- 10 January 2022
- Published by Springer Nature in Research Square
A Modified ACO with K-OPT for Restricted Covering Salesman Problems in Different Environments
In this study, the ant colony optimization (ACO) algorithm is modified with the K-opt operation to solve the covering salesman problem(CSP) under one restriction in crisp and imprecise (fuzzy, rough) environments. A CSP involves two phases- the division of cities into groups with the selection of the visiting cities and searching of the Hamiltonian circuit through the visiting cities. But, none of the studies in the literature is made following the direct approach. Also, none of the studies in the literature gives attention to reduce the total travel distance of the unvisited cities from the visited city of a group. Moreover, there is no algorithm in the literature which provides the solution of a CSP with the specified coverage range $r$. Also, none has introduced any algorithm to solve CSPs in imprecise environments. Though algorithms are available to solve the Traveling Salesman Problems in the imprecise environments, the approach cannot deal with the problems involving fuzzy data with non-linear membership functions or the problems involving rough data where the rough estimation can not be done using Lebesgue measure. The well establish algorithm for any routing problem is the ACO, but not much attention has been paid to solve the CSP using ACOs. To overcome these limitations on the studies of the ACO on the CSPs, here, an algorithm is proposed for the division of groups of the set of cities depending upon the maximum number of cities in a group and the total number of groups. Then ACO is used to find the shortest/minimum-cost path of the problem by selecting only one visiting the city from each group without violating the restriction of the specified coverage range $r$ of the location of the unvisited cities. K-opt operation is applied periodically at the end of ACO operation to improve the quality of the best found solution so far by the ACO algorithm and to arrest any premature convergence. For the restricted problems paths are searched in such a manner that the total distance/travel cost of different unvisited cities of a group from the visited city of the group should not exceed a predefined upper limit. To solve the problem in an imprecise environment some approach is followed so that the tour is searched without transferring the imprecise optimisation problem into an equivalent crisp optimisation problem. Also, the simulation approaches in fuzzy and rough environments are proposed to deal with the CSPs with any type of estimation of the imprecise data set. Algorithm is tested with the standard benchmark crisp problems available in the literature. To test the algorithm in the imprecise environments, the imprecise instances are derived randomly from the standard crisp instances using a specified rule. Test results imply that the proposed algorithm is efficient enough in solving the CSPs in the crisp as well as in the imprecise environments.- 10 January 2022
- Published by Springer Nature in Research Square
Open AccessA Modified ACO with K-OPT for Restricted Covering Salesman Problems in Different Environments
In this study, the ant colony optimization (ACO) algorithm is modified with the K-opt operation to solve the covering salesman problem(CSP) under one restriction in crisp and imprecise (fuzzy, rough) environments. A CSP involves two phases- the division of cities into groups with the selection of the visiting cities and searching of the Hamiltonian circuit through the visiting cities. But, none of the studies in the literature is made following the direct approach. Also, none of the studies in the literature gives attention to reduce the total travel distance of the unvisited cities from the visited city of a group. Moreover, there is no algorithm in the literature which provides the solution of a CSP with the specified coverage range $r$. Also, none has introduced any algorithm to solve CSPs in imprecise environments. Though algorithms are available to solve the Traveling Salesman Problems in the imprecise environments, the approach cannot deal with the problems involving fuzzy data with non-linear membership functions or the problems involving rough data where the rough estimation can not be done using Lebesgue measure. The well establish algorithm for any routing problem is the ACO, but not much attention has been paid to solve the CSP using ACOs. To overcome these limitations on the studies of the ACO on the CSPs, here, an algorithm is proposed for the division of groups of the set of cities depending upon the maximum number of cities in a group and the total number of groups. Then ACO is used to find the shortest/minimum-cost path of the problem by selecting only one visiting the city from each group without violating the restriction of the specified coverage range $r$ of the location of the unvisited cities. K-opt operation is applied periodically at the end of ACO operation to improve the quality of the best found solution so far by the ACO algorithm and to arrest any premature convergence. For the restricted problems paths are searched in such a manner that the total distance/travel cost of different unvisited cities of a group from the visited city of the group should not exceed a predefined upper limit. To solve the problem in an imprecise environment some approach is followed so that the tour is searched without transferring the imprecise optimisation problem into an equivalent crisp optimisation problem. Also, the simulation approaches in fuzzy and rough environments are proposed to deal with the CSPs with any type of estimation of the imprecise data set. Algorithm is tested with the standard benchmark crisp problems available in the literature. To test the algorithm in the imprecise environments, the imprecise instances are derived randomly from the standard crisp instances using a specified rule. Test results imply that the proposed algorithm is efficient enough in solving the CSPs in the crisp as well as in the imprecise environments.