Ant Colony Optimization for Mixed-Variable Optimization Problems

Liao, Tianjun; Socha, Krzysztof; de, Marco A. Montes; Stützle, Thomas; Dorigo, Marco

doi:10.1109/tevc.2013.2281531

Cited by 220 publications

(102 citation statements)

References 63 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…The problems of simultaneous handling of quantitative and ordinal data have got least attention of the researchers. Only Hsieh and Tong (2001), Wu (2008) and Liao et al (2014) have attempted to optimize simultaneously the quantitative and ordinal responses. Hsieh and Tong (2001) applied artificial neural networks for simultaneous optimization of quantitative and ordinal responses.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, availability of an appropriate methodology which is capable of optimizing all the quantitative and ordinal variables simultaneously can truly satisfy the practical need of the industries. It is observed that only Hsieh and Tong (2001), Wu (2008) and Liao et al (2014) have attempted to optimize quantitative and ordinal response variables simultaneously under Taguchi's framework of robust design approach. Hsieh and Tong (2001) applied artificial neural networks for simultaneous optimization of quantitative and ordinal responses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A desirability functions-based approach for simultaneous optimization of quantitative and ordinal response variables in industrial processes

Pal¹,

Gauri²

2018

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

The most important step for producing high quality products is the optimum utilization of the manufacturing processes and its resources, which can be accomplished by using optimal process settings. Extensive research works are reported in literature for optimization of process settings with respect to single as well as multiple quantitative response variables. However, in real world, often some aspects of product quality (e.g. dimensions, yield etc.) are measured quantitatively and some other aspects of product quality (e.g. color, finish etc.) are assessed qualitatively like 'poor', 'average', 'good' etc., which can be treated as ordered categorical or ordinal variables. Only a few researchers have attempted to tackle the problem of simultaneous optimization of quantitative and ordinal response variables. But none of these approaches results in an efficient optimal solution with respect to the desirability values of all the individual response variables. It motivated us to develop a more useful approach for simultaneously optimizing industrial processes with respect to quantitative as well as ordinal response variables. In the proposed method, the concept of desirability functions for continuous quantitative variables is extended for ordinal response variables, and then process settings is optimized considering geometric mean of the individual desirability functions of continuous as well as ordinal response variables. A set of experimental data, which have been analyzed by the past researchers, are analyzed using the proposed method and the related results are compared. The comparison of results reveals that the proposed method leads to better optimal solution than the other methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A desirability functions-based approach for simultaneous optimization of quantitative and ordinal response variables in industrial processes

Pal¹,

Gauri²

2018

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

show abstract

“…ACO MV [3] is applied to mixed variables optimization problems with r real-valued variables, c categorical-valued variables and o ordinal-valued variables. The ACO MV uses a solution archive (SA) as a form of pheromone model, instead of a pheromone matrix.…”

Section: Aco MVmentioning

confidence: 99%

“…In this paper we propose a new approach to extract IF-THEN of classification rules, based on the ACO for mixedvariable optimization (ACO MV ) [3]. Our approach handles the mixed attributes directly: attributes are categorized as continuous, ordinal and categorical attributes.…”

Section: Introductionmentioning

confidence: 99%

A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm

Helal

Otero

2016

Proceedings of the Genetic and Evolutionary Computation Conference 2016

View full text Add to dashboard Cite

In this paper, we introduce Ant-Miner MA to tackle mixedattribute classification problems. Most classification problems involve continuous, ordinal and categorical attributes. The majority of Ant Colony Optimization (ACO) classification algorithms have the limitation of being able to handle categorical attributes only, with few exceptions that use a discretisation procedure when handling continuous attributes either in a preprocessing stage or during the rule creation. Using a solution archive as a pheromone model, inspired by the ACO for mixed-variable optimization (ACO MV ), we eliminate the need for a discretisation procedure and attributes can be treated directly as continuous, ordinal, or categorical. We compared the proposed Ant-Miner MA against cAnt-Miner, an ACO-based classification algorithm that uses a discretisation procedure in the rule construction process. Our results show that Ant-Miner MA achieved significant improvements on computational time due to the elimination of the discretisation procedure without affecting the predictive performance.

show abstract

“…There have been many comparisons made between the performance of BABC and other optimization algorithms such as Genetic Algorithm (GA) [40], Genetic Programming (GP) [40][41], Evolutionary Strategy (ES) [42], Evolutionary Programming (EP) [40,43], PSO and also Ant Colony Optimization (ACO) [44][45]. In those comparisons, ABC has been proven to outperform the other algorithms in solving multimodal and multidimensional optimization problems with using less control parameters [35][36][37] and the ability to escape from local minima [38].…”

Section: Introductionmentioning

confidence: 99%

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

Zabidi¹,

Yassin²,

Tahir³

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

This paper presents a comparison between the Binary Artificial Bee Colony (BABC) and Binary Particle Swarm Optimization (BPSO) algorithm for structure selection of a Nonlinear Auto-Regressive Model (NAR) of the chaotic Mackey optimization algorithms are swarm BPSO mimicking the swarming behavior of birds. Recent research has suggested that the ABC algorithm has better solution quality compared to PSO. However, resea this advantage applies to the structure selection case in system identification has not been investigated. We conduct extensive tests to determine the convergence performance of both algorithms.Author Correspondence, e-mail: . Zabidi et al. J Fundam Appl Sci. 2017, 9(3S), 730-754 731 It found that the BABC had managed to significantly outperform BPSO in terms of convergence consistency with a slight advantage in terms of solution quality.

show abstract

Ant Colony Optimization for Mixed-Variable Optimization Problems

Cited by 220 publications

References 63 publications

A desirability functions-based approach for simultaneous optimization of quantitative and ordinal response variables in industrial processes

A desirability functions-based approach for simultaneous optimization of quantitative and ordinal response variables in industrial processes

A Mixed-Attribute Approach in Ant-Miner Classification Rule Discovery Algorithm

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

Contact Info

Product

Resources

About