A Binary Harmony Search Algorithm for Solving the Maximum Clique Problem

Afkhami, Sepideh; Marouzi, Omid Reza; Soleimani, Ali

doi:10.5120/11897-7956

Cited by 4 publications

(6 citation statements)

References 20 publications

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“…HS has been successfully applied to various discrete optimization problems such as Maximum Clique Problem (Afkhami et al 2013), traveling salesperson problem (Geem et al 2001), tour routing (Geem et al 2005), water network design (Geem 2006), Dynamic relocation of mobile base stations in wireless sensor networks (Moh'd Alia 2017), and others.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Improving rule based classification using harmony search

Hasanpour

Meibodi

Navi

2019

Preprint

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Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, and etc. Numerous previous studies have shown that this type of classifiers achieves high classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, harmony search, and classification based association rules (CBA) algorithm for building a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary harmony search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

“…This is due to the pitch adjusting operator not being able to perform the local search in the binary space. Therefore we used the implementation of HS that proposed by Afkhami et al (Afkhami et al 2013).…”

Section: Proposed Methodsmentioning

confidence: 99%

Improving rule based classification using harmony search

Hasanpour

Meibodi

Navi

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…If the stopping criteria we not satisfied, go to step 2. HS has been successfully applied to various discrete optimization problems such as Maximum Clique Problem (Afkhami, Ma & Soleimani, 2013), traveling salesperson problem (Geem, Kim & Loganathan, 2001), tour routing (Geem, Tseng & Park, 2005), water network design (Geem, 2006), dynamic relocation of mobile base stations in wireless sensor networks (Moh'd Alia, 2017), and others.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…This is due to the pitch adjusting operator not being able to perform the local search in the binary space. Therefore we used the implementation of HS that proposed by Afkhami, Ma & Soleimani (2013).…”

Section: Proposed Methodsmentioning

confidence: 99%

Improving rule-based classification using Harmony Search

Hasanpour

Meibodi

Navi

2019

PeerJ Computer Science

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Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, etc. Numerous previous studies have shown that this type of classifier achieves a higher classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, Harmony Search, and classification-based association rules (CBA) algorithm in order to build a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary Harmony Search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on a seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

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“…Therefore, binary algorithms are needed to solve discrete problems better. The binary version of many meta-heuristic algorithms, including binary particle swarm optimization (BPSO) [29], binary bat algorithm (BBA) [30], binary gray wolf optimizer (BGWO) [31], binary harmony search (BHS) [32], binary differential evolution (BDE) [33], binary magnetic optimization (BMOA) [34], and binary gravitational search algorithm (BGSA) [35], has been developed to deal with binary problems. Each of these algorithms has strengths and weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Binary Chimp Optimization Algorithm (BChOA): a New Binary Meta-heuristic for Solving Optimization Problems

Wang¹,

Khishe²,

Kaveh

et al. 2021

Cogn Comput

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Chimp optimization algorithm (ChOA) is a newly proposed meta-heuristic algorithm inspired by chimps' individual intelligence and sexual motivation in their group hunting. The preferable performance of ChOA has been approved among other well-known meta-heuristic algorithms. However, its continuous nature makes it unsuitable for solving binary problems. Therefore, this paper proposes a novel binary version of ChOA and attempts to prove that the transfer function is the most important part of binary algorithms. Therefore, four S-shaped and V-shaped transfer functions, as well as a novel binary approach, have been utilized to investigate the efficiency of binary ChOAs (BChOA) in terms of convergence speed and local minima avoidance. In this regard, forty-three unimodal, multimodal, and composite optimization functions and ten IEEE CEC06-2019 benchmark functions were utilized to evaluate the efficiency of BChOAs. Furthermore, to validate the performance of BChOAs, four newly proposed binary optimization algorithms were compared with eighteen novel stateof-the-art algorithms. The results indicate that both the novel binary approach and V-shaped transfer functions improve the efficiency of BChOAs in a statistically significant way.

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A Binary Harmony Search Algorithm for Solving the Maximum Clique Problem

Cited by 4 publications

References 20 publications

Improving rule based classification using harmony search

Improving rule based classification using harmony search

Improving rule-based classification using Harmony Search

Binary Chimp Optimization Algorithm (BChOA): a New Binary Meta-heuristic for Solving Optimization Problems

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