Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem

Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features

Optimization of attribute selection model using bio-inspired algorithms

Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms.Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction

Stochastic local search algorithms for single and bi-objective quadratic assignment problems

The study of Stochastic Local Search (SLS) algorithms is becoming more pivotal these days, due to their vast number of applications in decision making. Prior to the implementation of algorithmic knowledge for decision making, many decisions were made based on manual calculation, on the fly, or even based on guts feeling. Nowadays, such an approach is more rarely seen, especially when the decisions that need to be made are high-risk, cost intensive, or time-consuming. The increasingly often used SLS algorithms are one of the options available to assist the decision making process these days.The work discussed in this thesis concerns the study of SLS algorithms for solving the Quadratic Assignment Problem (QAP), a prominent combinatorial optimization problem, which until today is very hard to solve. Our interest is to study the behavior and performance of SLS algorithms for solving QAP instances of different characteristics, such as size, sparsity, and structure. In this study, we have also proposed new variants of SLS algorithms, inspired by existing, well-performing SLS algorithms for solving the QAP. The new variants of SLS algorithms are then further extended for solving the bi-objective QAP (bQAP).One main focus in this study is to see how the performance of algorithms scales with instance size. We have considered instances that are much larger than the ones usually used in the studies of algorithms for solving the QAP. By understanding how the algorithms perform when the instance size changes, we might be able to solve other problems effectively by considering the similarity in their characteristics to the ones of the QAP, or by seeing common trends in the relative performance of the various available SLS methods. For single objective QAP instances we found that the structure and size of instances do have a significant impact on the performance of SLS algorithms. For example, comparisons between Tabu Search (TS) and Simulated Annealing (SA) on instances with randomly generated matrices show that the overall performance of TS is better than SA, irrespective the size of instances considered. The results on a class of structured instances however show that TS performs well on small-sized instances, while on the larger ones, SA shows better results. In another experiment, Hierarchical Iterated Local Search (HILS) has shown very good results compared to several Iterated Local Search (ILS) variants. This experiment was done on a class of structured instances of size from 100 to 500. An extensive experiment on a class of structured instances of size 30 to 300 using tuned parameter settings shows that population based algorithms perform very well on most of the instance classes considered. SA however, shows very good performance especially on large-sized instances with low sparsity level. For the bQAP, the correlation between the flow matrices does have a strong effect that determines the performance of algorithms for solving them. Hybrid Simulated Annealing (HSA) clearly outperforms Hybrid Iterative Improvement (HII). When compared to Multi Objective Ant Colony Optimization (MOACO) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), HSA shows very good performance, where HSA outperforms MOACO and SPEA2, especially on instances of large size, thus, offering a better scaling behavior. Based the results obtained in this study, it is possible to come up with a general idea on the suitability of SLS algorithms for solving instances with a certain characteristic. Given an unknown QAP instance, one can guess the most suitable algorithm for solving it depending on the type, size, and sparsity of the instance, while for a bQAP instance the most suitable algorithm can be guessed based on its size and correlation between the flow matrices.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Stochastic local search algorithms for single and bi-objective quadratic assignment problems

The study of Stochastic Local Search (SLS) algorithms is becoming more pivotal these days, due to their vast number of applications in decision making. Prior to the implementation of algorithmic knowledge for decision making, many decisions were made based on manual calculation, on the fly, or even based on guts feeling. Nowadays, such an approach is more rarely seen, especially when the decisions that need to be made are high-risk, cost intensive, or time-consuming. The increasingly often used SLS algorithms are one of the options available to assist the decision making process these days.The work discussed in this thesis concerns the study of SLS algorithms for solving the Quadratic Assignment Problem (QAP), a prominent combinatorial optimization problem, which until today is very hard to solve. Our interest is to study the behavior and performance of SLS algorithms for solving QAP instances of different characteristics, such as size, sparsity, and structure. In this study, we have also proposed new variants of SLS algorithms, inspired by existing, well-performing SLS algorithms for solving the QAP. The new variants of SLS algorithms are then further extended for solving the bi-objective QAP (bQAP).One main focus in this study is to see how the performance of algorithms scales with instance size. We have considered instances that are much larger than the ones usually used in the studies of algorithms for solving the QAP. By understanding how the algorithms perform when the instance size changes, we might be able to solve other problems effectively by considering the similarity in their characteristics to the ones of the QAP, or by seeing common trends in the relative performance of the various available SLS methods. For single objective QAP instances we found that the structure and size of instances do have a significant impact on the performance of SLS algorithms. For example, comparisons between Tabu Search (TS) and Simulated Annealing (SA) on instances with randomly generated matrices show that the overall performance of TS is better than SA, irrespective the size of instances considered. The results on a class of structured instances however show that TS performs well on small-sized instances, while on the larger ones, SA shows better results. In another experiment, Hierarchical Iterated Local Search (HILS) has shown very good results compared to several Iterated Local Search (ILS) variants. This experiment was done on a class of structured instances of size from 100 to 500. An extensive experiment on a class of structured instances of size 30 to 300 using tuned parameter settings shows that population based algorithms perform very well on most of the instance classes considered. SA however, shows very good performance especially on large-sized instances with low sparsity level. For the bQAP, the correlation between the flow matrices does have a strong effect that determines the performance of algorithms for solving them. Hybrid Simulated Annealing (HSA) clearly outperforms Hybrid Iterative Improvement (HII). When compared to Multi Objective Ant Colony Optimization (MOACO) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), HSA shows very good performance, where HSA outperforms MOACO and SPEA2, especially on instances of large size, thus, offering a better scaling behavior. Based the results obtained in this study, it is possible to come up with a general idea on the suitability of SLS algorithms for solving instances with a certain characteristic. Given an unknown QAP instance, one can guess the most suitable algorithm for solving it depending on the type, size, and sparsity of the instance, while for a bQAP instance the most suitable algorithm can be guessed based on its size and correlation between the flow matrices.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

High Performing Stochastic Local Search Algorithms for the QAP and their Performance in Dependence to the Instance Structure and Sized

info:eu-repo/semantics/publishe

Hybrid simulated annealing for the bi-objective quadratic assignment problem

Past research has shown that the performance of algorithms for solving the Quadratic Assignment Problem (QAP) depends on the structure and the size of the instances. In this paper, we study the bi-objective QAP, which is a multi-objective extension of the single-objective QAP to two objectives. The algorithm we propose extends a high-performing Simulated Annealing (SA) algorithm for large-sized, single-objective QAP instances to the bi-objective context. The resulting Hybrid Simulated Annealing (HSA) algorithm is shown to clearly outperform a basic, hybrid iterative improvement algorithm. Experimental results show that HSA clearly outperforms basic Hybrid Iterative Improvement. When compared to state-of-the-art algorithms for the bQAP, a Multi-objective Ant Colony Optimization algorithm and the Strength Pareto Evolutionary Algorithm 2, HSA shows very good performance, outperforms the former in most cases, and showing competitive performance to the latter.SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Tabu search vs. simulated annealing as a function of the size of quadratic assignment problem instances

Performance comparisons between algorithms have a long tradition in metaheuristic research. An early example is comparisons between Tabu Search (TS) and Simulated Annealing (SA) algorithms for tackling the Quadratic Assignment Problem (QAP). The results of these comparisons are to a certain extent inconclusive, even when focusing on only these two types of algorithms. While comparisons of SA and TS algorithms were based on rather small-sized instances, here we focus on possible dependencies of the relative performance between SA and TS algorithms on instance size. In fact, our experimental results show that the assertion whether one algorithm is better than the other can depend strongly on QAP instance size even if one focuses on instances with otherwise same characteristics. © 2013 Elsevier Ltd.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A study of stochastic local search algorithms for the quadratic assignment problem

info:eu-repo/semantics/publishe

Ideal combination feature selection model for classification problem based on bio-inspired approach

Feature selection or attribute reduction is a crucial process to achieve optimal data reduction for classification task. However, most of the feature selection methods that were introduced work individually that sometimes caused less optimal feature being selected, subsequently degrading the consistency of the classification accuracy rate. The aim of this paper is to exploit the capability of bio-inspired search algorithms, together with wrapper and filtered methods in generating optimal set of features. The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. The next step is to define an optimized feature set for classification task. Performance metrics are analyzed based on classification accuracy and the number of selected features. Experiments were conducted on nine (9) benchmark datasets with various sizes, categorized as small, medium and large dataset. Experimental results revealed that the ideal combination is a feature selection model with the implementation of bio-inspired search algorithm that consistently obtains the optimal solution (i.e. less number of features with higher classification accuracy) on the selected dataset. Such a finding indicates that the exploitation of bio-inspired algorithms with ideal combination of wrapper/filtered method can contribute in finding the optimal features to be used in data mining model construction