A bee-inspired algorithm for optimal data clustering

Cruz, Dávila Patrícia Ferreira; Maia, Renato Dourado; Szabó, Adrienn; Castro, Leandro Nunes de

doi:10.1109/cec.2013.6557953

Cited by 13 publications

(10 citation statements)

References 22 publications

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“…[71] cOptBees was first presented by Cruz et al [4] and the algorithm was applied to different clustering problems, being capable of finding optimal clusters, generating and maintaining the diversity of solutions, and finding the correct number of clusters. A parametric sensitivity analysis of cOptBees was presented by Cruz et al [72] and it was applied to training a Radial Basis Function (RBF) neural network.…”

Section: Coptbees-mo: a Multiobjective Bee-inspired Clustering Algorithmmentioning

confidence: 99%

“…The method summarized in Algorithm 1, is an extension of cOptBees, [4,70] a monoobjective clustering algorithm able to generate and maintain diversity of candidate solutions in a way to find multiple local optima without compromising its global search capability. [71] cOptBees was first presented by Cruz et al [4] and the algorithm was applied to different clustering problems, being capable of finding optimal clusters, generating and maintaining the diversity of solutions, and finding the correct number of clusters.…”

Section: Coptbees-mo: a Multiobjective Bee-inspired Clustering Algorithmmentioning

confidence: 99%

“…[21,22] The main approaches to MOO are: [23] 1) transformation of the multiobjective problem into a mono objective one by means of a weighted formula; 2) ordering the objectives according to their importance; and 3) the Pareto approach, where multiobjective functions are optimized simultaneously to generate a resultant set with a number of non-dominated solutions. [23] Population-based algorithms, for example genetic, [24,25] bee inspired [4,26] and ant colony [27] algorithms are particularly suited for approximating the entire Pareto front because they work with a pool of solutions rather than a single candidate solution. This enables approximating several solutions of the Pareto set simultaneously in a unique algorithm run.…”

Section: Multiobjective Clustering Opti-mization (Moco)mentioning

confidence: 99%

“…This paper proposes a multiobjective clustering algorithm inspired by the foraging behavior of bee colonies, called cOptBees-MO, which is a multiobjective version for the cOptBees clustering algorithm. [4] cOptBees is able to find optimal clustering within datasets, maintain the diversity of solutions and estimate the adequate number of clusters. The cOptBees-MO, as well as its parent algorithm, manipulates a swarm of artificial bees who play different roles during the search phase.…”

Section: Introductionmentioning

confidence: 99%

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Bio-inspired multiobjective clustering optimization: A survey and a proposal

Cunha

Cruz

Politi

et al. 2017

AIR

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Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.

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Section: Coptbees-mo: a Multiobjective Bee-inspired Clustering Algorithmmentioning

confidence: 99%

Section: Coptbees-mo: a Multiobjective Bee-inspired Clustering Algorithmmentioning

confidence: 99%

Section: Multiobjective Clustering Opti-mization (Moco)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bio-inspired multiobjective clustering optimization: A survey and a proposal

Cunha

Cruz

Politi

et al. 2017

AIR

Self Cite

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“…It was later applied to other optimization problems, such as data clustering [91] and the training of artificial neural networks [92]. The TSPoptBees is a modification of the original optBees algorithm to solve the travelling salesman problem.…”

Section: Tspoptbees: a Bee-inspired Algorithm For Solving The Travellmentioning

confidence: 99%

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

Masutti

Castro

2017

Mathematical Problems in Engineering

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Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

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A Comparative Analysis Between Crisp and Fuzzy Data Clustering Approaches for Traditional and Bioinspired Algorithms

Coradini

Szabó

2020

Lecture Notes in Computer Science

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A bee-inspired algorithm for optimal data clustering

Cited by 13 publications

References 22 publications

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

A Comparative Analysis Between Crisp and Fuzzy Data Clustering Approaches for Traditional and Bioinspired Algorithms

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