The Experts below are selected from a list of 93 Experts worldwide ranked by ideXlab platform
Shyam Sundar - One of the best experts on this subject based on the ideXlab platform.
-
Two new heuristics for the dominating tree problem
Applied Intelligence, 2017Co-Authors: Kavita Singh, Shyam SundarAbstract:Dominating Tree Problem (DTP) aims to find a dominating tree (d T r e e) of minimum cost on a given connected, undirected and weighted graph in such a way that a vertex in the graph is either in d T r e e or adjacent to a vertex in d T r e e. A Solution (d T r e e) to this problem can be used as routing backbone in wireless sensor network. Being a \(\mathcal {NP}\)-Hard problem, several problem-specific heuristics and metaheuristic techniques have been proposed. This paper presents two new heuristics for the DTP. First one is a new problem-specific heuristic that exploits the problem structure effectively, whereas the other is an artificial bee colony (ABC) algorithm. The proposed ABC for the DTP is different from the existing ABC algorithm for the DTP in the literature on its two main components: initial Solution generation, and determining a Neighboring Solution. Computational results show on a set of standard benchmark instances that the proposed problem-specific heuristic and ABC algorithm for the DTP demonstrate the superiority over all existing problem-specific heuristics and metaheuristic techniques respectively in the literature.
-
Two grouping-based metaheuristics for clique partitioning problem
Applied Intelligence, 2017Co-Authors: Shyam Sundar, Alok SinghAbstract:Given a connected, undirected graph G = (V,E), where V is the set of vertices and E is the set of edges, the clique partitioning problem (CPP) seeks on this graph a partition of set V into minimum number of subsets such that each subset is a clique. The CPP is an \(\mathcal {NP}\)-Hard problem and finds numerous practical applications in diverse domains like digital design synthesis, clustering etc. Despite its computational complexity and its numerous applications, only problem-specific heuristics have been developed so far for this problem in the literature. In this paper, two metaheuristic techniques – a steady-state grouping genetic algorithm and an artificial bee colony algorithm – are proposed for the CPP. Both the proposed approaches are designed in such a way that the grouping structure of the CPP is exploited effectively while generating new Solutions. Since artificial bee colony algorithm is comparatively a new metaheuristic technique, special attention has been given to the design of this algorithm for the CPP and we came out with a new Neighboring Solution generation method utilizing Solution components from multiple Solutions. The proposed approaches have been tested on publicly available 37 DIMACS graph instances. Computational results show the effectiveness of the proposed approaches.
-
A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint
Soft Computing, 2015Co-Authors: Shyam Sundar, Ponnuthurai Nagaratnam Suganthan, Chua Tay Jin, Cai Tian Xiang, Chong Chin SoonAbstract:This paper studies a hybrid artificial bee colony (ABC) algorithm for finding high quality Solutions of the job-shop scheduling problem with no-wait constraint (JSPNW) with the objective of minimizing makespan among all the jobs. JSPNW is an extension of well-known job-shop scheduling problem subject to the constraint that no waiting time is allowed between operations for a given job. ABC algorithm is a swarm intelligence technique based on intelligent foraging behavior of honey bee swarm. The proposed hybrid approach effectively coordinates the various components of ABC algorithm such as Solution initialization, selection and determination of a Neighboring Solution with the local search in such a way that it leads to high quality Solutions for the JSPNW. The proposed approach is compared with the two best approaches in the literature on a set of benchmark instances. Computational results show the superiority of the proposed approach over these two best approaches.
Tiranee Achalakul - One of the best experts on this subject based on the ideXlab platform.
-
The best-so-far ABC with multiple patrilines for clustering problems
Neurocomputing, 2013Co-Authors: Anan Banharnsakun, Booncharoen Sirinaovakul, Tiranee AchalakulAbstract:Clustering is an important process in many application domains such as machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics. The main objective of clustering is to search for hidden patterns that may exist in datasets. Since the clustering problem is considered to be NP-hard, previous research has applied bio-inspired heuristic methods to solve such problems. In this paper we propose an effective method for clustering using an algorithm inspired by the decision making processes of bee swarms. The algorithm is called the Best-so-far Artificial Bee Colony with multiple patrilines. In the Best-so-far method, the Solution direction is biased toward the Best-so-far Solution rather than a Neighboring Solution proposed in the original Artificial Bee Colony algorithm. We introduce another bee-inspired concept called multiple patrilines to further improve the diversity of Solutions and allow the calculations to be distributed among multiple computing units. We empirically assess the performance of our proposed method on several standard datasets taken from the UCI Machine Learning Repository. The results show that the proposed method produces Solutions that are as good as or better than the current state-of-the-art clustering techniques reported in the literature. Furthermore, to demonstrate the computing performance and scalability of the algorithm, we assess the algorithm on a large disk drive manufacturing dataset. The results indicate that our distributed Best-so-far approach is scalable and produces good Solutions while significantly improving the processing time.
-
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence, 2012Co-Authors: Anan Banharnsakun, Booncharoen Sirinaovakul, Tiranee AchalakulAbstract:The Job Shop Scheduling Problem (JSSP) is known as one of the most difficult scheduling problems. It is an important practical problem in the fields of production management and combinatorial optimization. Since JSSP is NP-complete, meaning that the selection of the best scheduling Solution is not polynomially bounded, heuristic approaches are often considered. Inspired by the decision making capability of bee swarms in the nature, this paper proposes an effective scheduling method based on Best-so-far Artificial Bee Colony (Best-so-far ABC) for solving the JSSP. In this method, we bias the Solution direction toward the Best-so-far Solution rather a Neighboring Solution as proposed in the original ABC method. We also use the set theory to describe the mapping of our proposed method to the problem in the combinatorial optimization domain. The performance of the proposed method is then empirically assessed using 62 benchmark problems taken from the Operations Research Library (OR-Library). The Solution quality is measured based on ''Best'', ''Average'', ''Standard Deviation (S.D.)'', and ''Relative Percent Error (RPE)'' of the objective value. The results demonstrate that the proposed method is able to produce higher quality Solutions than the current state-of-the-art heuristic-based algorithms.
Anan Banharnsakun - One of the best experts on this subject based on the ideXlab platform.
-
The best-so-far ABC with multiple patrilines for clustering problems
Neurocomputing, 2013Co-Authors: Anan Banharnsakun, Booncharoen Sirinaovakul, Tiranee AchalakulAbstract:Clustering is an important process in many application domains such as machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics. The main objective of clustering is to search for hidden patterns that may exist in datasets. Since the clustering problem is considered to be NP-hard, previous research has applied bio-inspired heuristic methods to solve such problems. In this paper we propose an effective method for clustering using an algorithm inspired by the decision making processes of bee swarms. The algorithm is called the Best-so-far Artificial Bee Colony with multiple patrilines. In the Best-so-far method, the Solution direction is biased toward the Best-so-far Solution rather than a Neighboring Solution proposed in the original Artificial Bee Colony algorithm. We introduce another bee-inspired concept called multiple patrilines to further improve the diversity of Solutions and allow the calculations to be distributed among multiple computing units. We empirically assess the performance of our proposed method on several standard datasets taken from the UCI Machine Learning Repository. The results show that the proposed method produces Solutions that are as good as or better than the current state-of-the-art clustering techniques reported in the literature. Furthermore, to demonstrate the computing performance and scalability of the algorithm, we assess the algorithm on a large disk drive manufacturing dataset. The results indicate that our distributed Best-so-far approach is scalable and produces good Solutions while significantly improving the processing time.
-
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence, 2012Co-Authors: Anan Banharnsakun, Booncharoen Sirinaovakul, Tiranee AchalakulAbstract:The Job Shop Scheduling Problem (JSSP) is known as one of the most difficult scheduling problems. It is an important practical problem in the fields of production management and combinatorial optimization. Since JSSP is NP-complete, meaning that the selection of the best scheduling Solution is not polynomially bounded, heuristic approaches are often considered. Inspired by the decision making capability of bee swarms in the nature, this paper proposes an effective scheduling method based on Best-so-far Artificial Bee Colony (Best-so-far ABC) for solving the JSSP. In this method, we bias the Solution direction toward the Best-so-far Solution rather a Neighboring Solution as proposed in the original ABC method. We also use the set theory to describe the mapping of our proposed method to the problem in the combinatorial optimization domain. The performance of the proposed method is then empirically assessed using 62 benchmark problems taken from the Operations Research Library (OR-Library). The Solution quality is measured based on ''Best'', ''Average'', ''Standard Deviation (S.D.)'', and ''Relative Percent Error (RPE)'' of the objective value. The results demonstrate that the proposed method is able to produce higher quality Solutions than the current state-of-the-art heuristic-based algorithms.
Alok Singh - One of the best experts on this subject based on the ideXlab platform.
-
an artificial bee colony algorithm with variable degree of perturbation for the generalized covering traveling salesman problem
Applied Soft Computing, 2019Co-Authors: Venkatesh Pandiri, Alok SinghAbstract:Abstract The generalized covering traveling salesman problem is a recently introduced variant of the traveling salesman problem. Given a set of cities that includes depot, facilities and customer cities, starting at the depot, the salesman has to visit a subset of facilities in order to cover some of the customers so that a constraint function based on customer coverage is satisfied. The goal is to minimize the total distance traveled by the salesman. A customer is covered if it is within a coverage radius r i of a facility i visited by the salesman. This problem finds important applications in humanitarian relief transportation and telecommunication networks. In this paper, we have proposed an artificial bee colony algorithm with variable degree of perturbation for this problem where the degree to which a Solution is perturbed for generating its Neighboring Solution is reduced over iterations. Computational results on a wide range of benchmark instances and their analysis show the effectiveness of our proposed approach in comparison to other state-of-the-art approaches.
-
Two grouping-based metaheuristics for clique partitioning problem
Applied Intelligence, 2017Co-Authors: Shyam Sundar, Alok SinghAbstract:Given a connected, undirected graph G = (V,E), where V is the set of vertices and E is the set of edges, the clique partitioning problem (CPP) seeks on this graph a partition of set V into minimum number of subsets such that each subset is a clique. The CPP is an \(\mathcal {NP}\)-Hard problem and finds numerous practical applications in diverse domains like digital design synthesis, clustering etc. Despite its computational complexity and its numerous applications, only problem-specific heuristics have been developed so far for this problem in the literature. In this paper, two metaheuristic techniques – a steady-state grouping genetic algorithm and an artificial bee colony algorithm – are proposed for the CPP. Both the proposed approaches are designed in such a way that the grouping structure of the CPP is exploited effectively while generating new Solutions. Since artificial bee colony algorithm is comparatively a new metaheuristic technique, special attention has been given to the design of this algorithm for the CPP and we came out with a new Neighboring Solution generation method utilizing Solution components from multiple Solutions. The proposed approaches have been tested on publicly available 37 DIMACS graph instances. Computational results show the effectiveness of the proposed approaches.
Booncharoen Sirinaovakul - One of the best experts on this subject based on the ideXlab platform.
-
The best-so-far ABC with multiple patrilines for clustering problems
Neurocomputing, 2013Co-Authors: Anan Banharnsakun, Booncharoen Sirinaovakul, Tiranee AchalakulAbstract:Clustering is an important process in many application domains such as machine learning, data mining, pattern recognition, image analysis, information retrieval, and bioinformatics. The main objective of clustering is to search for hidden patterns that may exist in datasets. Since the clustering problem is considered to be NP-hard, previous research has applied bio-inspired heuristic methods to solve such problems. In this paper we propose an effective method for clustering using an algorithm inspired by the decision making processes of bee swarms. The algorithm is called the Best-so-far Artificial Bee Colony with multiple patrilines. In the Best-so-far method, the Solution direction is biased toward the Best-so-far Solution rather than a Neighboring Solution proposed in the original Artificial Bee Colony algorithm. We introduce another bee-inspired concept called multiple patrilines to further improve the diversity of Solutions and allow the calculations to be distributed among multiple computing units. We empirically assess the performance of our proposed method on several standard datasets taken from the UCI Machine Learning Repository. The results show that the proposed method produces Solutions that are as good as or better than the current state-of-the-art clustering techniques reported in the literature. Furthermore, to demonstrate the computing performance and scalability of the algorithm, we assess the algorithm on a large disk drive manufacturing dataset. The results indicate that our distributed Best-so-far approach is scalable and produces good Solutions while significantly improving the processing time.
-
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence, 2012Co-Authors: Anan Banharnsakun, Booncharoen Sirinaovakul, Tiranee AchalakulAbstract:The Job Shop Scheduling Problem (JSSP) is known as one of the most difficult scheduling problems. It is an important practical problem in the fields of production management and combinatorial optimization. Since JSSP is NP-complete, meaning that the selection of the best scheduling Solution is not polynomially bounded, heuristic approaches are often considered. Inspired by the decision making capability of bee swarms in the nature, this paper proposes an effective scheduling method based on Best-so-far Artificial Bee Colony (Best-so-far ABC) for solving the JSSP. In this method, we bias the Solution direction toward the Best-so-far Solution rather a Neighboring Solution as proposed in the original ABC method. We also use the set theory to describe the mapping of our proposed method to the problem in the combinatorial optimization domain. The performance of the proposed method is then empirically assessed using 62 benchmark problems taken from the Operations Research Library (OR-Library). The Solution quality is measured based on ''Best'', ''Average'', ''Standard Deviation (S.D.)'', and ''Relative Percent Error (RPE)'' of the objective value. The results demonstrate that the proposed method is able to produce higher quality Solutions than the current state-of-the-art heuristic-based algorithms.
