Genetic algorithm with greedy heuristic for capacity planning

Kazakovtsev, Lev; Гудыма, М Н; Антамошкин, А. Н.

doi:10.1109/icumt.2014.7002170

Cited by 4 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the timetabling (scheduling) problems, the UCTP is one of the most complex problems, with many decision variables and various soft and hard constraints. Problems with formally simpler problem statements such as the industrial capacity planning [73,74] are sometimes large-scale, and the standard approach of reducing the problem to an integer linear programming problem leads to a huge increase in the number of variables, so in practice, it is necessary to apply various combinations of heuristic algorithms, including evolutionary algorithms, greedy search algorithms, and local search.…”

Section: Discussionmentioning

confidence: 99%

An Exploration and Exploitation-Based Metaheuristic Approach for University Course Timetabling Problems

Badoni

Sahoo

Srivastava

et al. 2023

Axioms

Self Cite

View full text Add to dashboard Cite

The university course timetable problem (UCTP) is known to be NP-hard, with solution complexity growing exponentially with the problem size. This paper introduces an algorithm that effectively tackles UCTPs by employing a combination of exploration and exploitation strategies. The algorithm comprises two main components. Firstly, it utilizes a genetic algorithm (GA) to explore the search space and discover a solution within the global optimum region. Secondly, it enhances the solution by exploiting the region using an iterated local search (ILS) algorithm. The algorithm is tested on two common variants of UCTP: the post-enrollment-based course timetable problem (PE-CTP) and the curriculum-based course timetable problem (CB-CTP). The computational results demonstrate that the proposed algorithm yields competitive outcomes when compared empirically against other existing algorithms. Furthermore, a t-test comparison with state-of-the-art algorithms is conducted. The experimental findings also highlight that the hybrid approach effectively overcomes the limitation of local optima, which is encountered when solely employing GA in conjunction with local search.

show abstract

Section: Discussionmentioning

confidence: 99%

An Exploration and Exploitation-Based Metaheuristic Approach for University Course Timetabling Problems

Badoni

Sahoo

Srivastava

et al. 2023

Axioms

Self Cite

View full text Add to dashboard Cite

show abstract

“…Algorithm 1, the Alternate Location-Allocation (ALA) procedure [7,23], is capable of solving both k-means and p-median problems, was introduced by Lloyd [3]. This procedure consistently improves an intermediate solution, which leads to finding locally optimal solutions.…”

Section: Known Approachesmentioning

confidence: 99%

Hybrid algorithms with alternative embedded local search schemes for the p-median problem

Rezova,

Kazakovtsev,

Rozhnov

et al. 2023

IJITS JOURNAL

View full text Add to dashboard Cite

Progress in the development of location theory and clustering methods is mainly aimed at improving the performance of algorithms. We study the continuous p-median problem with the Euclidean metric. Various algorithmic combinations of the ALA procedure embedded into more sophisticated algorithmic combinations are used. Two repeated steps of the ALA algorithm solve the simplest discrete optimization problem, and then a series of central point search problems (Weber problem) for each cluster. A parallel implementation of the ALA algorithm is used. The paper studies the issue of choosing from two strategies: solve the Weber problem until accuracy ε is achieved at each iteration, or perform only one iteration of the Weiszfeld algorithm at each ALA iteration.

show abstract

“…for the p-median network problem. This idea was inherited in (Kazakovtsev & Antamoshkin, 2014). Many mutation methods presented in (Kazakovtsev & Antamoshkin, 2014;Kwedlo & Iwanowicz, 2010) can be used in genetic algorithms for k-means and similar problems.…”

Section: Introductionmentioning

confidence: 99%

“…This idea was inherited in (Kazakovtsev & Antamoshkin, 2014). Many mutation methods presented in (Kazakovtsev & Antamoshkin, 2014;Kwedlo & Iwanowicz, 2010) can be used in genetic algorithms for k-means and similar problems. In the k-means algorithm, usually the initial solution is a subset of the original data.…”

Section: Introductionmentioning

confidence: 99%

Approach to Data Clustering Based on Molecular Chemical Reactions With Various Distance Measures

Markushin¹

2023

European Proceedings of Computers and Technology

View full text Add to dashboard Cite

Automatic clustering involves dividing a set of objects into subsets so that the objects from one subset are more similar to each other than to the objects from other subsets according to some criterion. The paper proposes an algorithm for clustering data using the k-means algorithm combined with molecular chemical reactions and with various types of distance measures: Euclidean distance, Squared Euclidean distance, Manhattan distance. This approach mimics a chemical reaction process in which reactants interact with one another. Every chemical reaction process generate a new molecular structure in the environment. By molecular structure, we mean a possible solution to data clustering, by optimizing the molecular chemical reactions we mean optimizing the results of data clustering (search for a global optimal solution). The solution obtained with k-means is used as an initial molecular structure solution to optimize chemical reactions by generating new solutions: single-molecule collision, single-molecule decomposition, intermolecular collision, and intermolecular synthesis. Computational experiments demonstrate the comparative efficiency and accuracy of using the k-means algorithm combined with molecular chemical reactions.

show abstract

Genetic algorithm with greedy heuristic for capacity planning

Cited by 4 publications

References 8 publications

An Exploration and Exploitation-Based Metaheuristic Approach for University Course Timetabling Problems

An Exploration and Exploitation-Based Metaheuristic Approach for University Course Timetabling Problems

Hybrid algorithms with alternative embedded local search schemes for the p-median problem

Approach to Data Clustering Based on Molecular Chemical Reactions With Various Distance Measures

Contact Info

Product

Resources

About