Hybridizing Harmony Search algorithm with different mutation operators for continuous problems

Hasan, Basima Hani; Doush, Iyad Abu; Maghayreh, Eslam Al; Alkhateeb, Faisal; Hamdan, Mohammad

doi:10.1016/j.amc.2013.12.139

Cited by 33 publications

(5 citation statements)

References 20 publications

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“…There are several mutation techniques that can be used in genetic algorithms like random mutation [ 18 ], boundary mutation [ 18 ], nonuniform random mutation [ 19 ], power mutation [ 17 ], polynomial mutation [ 18 ], and so forth. The experimental evaluation of the HS algorithm using different mutation methods using a well-known set of test functions shows that using the polynomial mutation improves the performance of the algorithm significantly for a considerable number of functions [ 20 ]. Deb and Agrawal [ 18 ] suggested a polynomial mutation operator with a user-defined index parameter ( η m ).…”

Section: Harmony Search Algorithmsmentioning

confidence: 99%

[Retracted] Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve‐Point Economic Load Dispatch

Muthusamy¹,

Raja²

2015

The Scientific World Journal

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Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods.

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Section: Harmony Search Algorithmsmentioning

confidence: 99%

[Retracted] Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve‐Point Economic Load Dispatch

Muthusamy¹,

Raja²

2015

The Scientific World Journal

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“…The control parameters of i CSPM2 were as follows: L = 1 in the Lévy flight method, PAR=0.3 in the PA method and the fraction of elite solutions p b was set to a value equal to the value of p a . The 15 benchmark functions described in Table 2 are well-recognized functions that have frequently been used to evaluate the efficiency of optimization algorithms [31,34]. These functions were used in Section 5.3 to test the sensitivity of i CSPM and i CSPM2 to the island model parameters.…”

Section: Algorithm 1 I Cspm With Elite Opposition-based Learning and Multiple Mutation Methods (I Cspm2)mentioning

confidence: 99%

“…Secondly, it uses CS with with HDP mutation (CS10) [2] to optimize the candidate solutions on each island. This is because, according to several experimental studies [31][32][33], CS10 is a better exploratory algorithm than the CS algorithm.…”

Section: Cuckoo Search With Jaya Mutationmentioning

confidence: 99%

Island-based Cuckoo Search with Elite Opposition-based Learning and Multiple Mutation Methods for Solving Discrete and Continuous Optimization Problems

Abed-alguni

Paul

2021

Preprint

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The Island Cuckoo Search ( i CSPM) algorithm is a new variation of Cuckoo Search (CS) that uses the island model and the Highly Disruptive Polynomial (HDP) mutation for solving a broad range of optimization problems. This article introduces an improved i CSPM algorithm called i CSPM with elite opposition-based learning and multiple mutation methods ( i CSPM2). i CSPM2 has three main characteristics. Firstly, it separates candidate solutions into a number of islands (sub-populations) and then divides the islands equally among four improved versions of CS: CS via Le'vy fights (CS1) [1], CS with HDPM mutation (CS10) [2], CS with Jaya mutation (CSJ) and CS with pitch adjustment mutation (CS11) [2]. Secondly, it uses Elite Opposition-based Learning (EOBL) to improve its convergence rate and exploration ability. Finally, it uses the Smallest Position Value (SPV) with scheduling problems to convert continuous candidate solutions into discrete ones. A set of 15 popular benchmark functions was used to compare the performance of iCSPM2 to the performance of the original i CSPM algorithm based on different experimental scenarios. Results indicate that i CSPM2 exhibits improved performance over i CSPM. However, the sensitivity analysis of i CSPM and i CSPM2 to their parameters indicates that their convergence behavior is sensitive to the island model parameters. Further, the single-objective IEEE CEC 2014 functions were used to evaluate and compare the performance of iCSPM2 to four well-known swarm optimization algorithms: DGWO [3], L-SHADE [4], MHDA [5] and FWA-DM [6]. The overall experimental and statistical results suggest that i CSPM2 has better performance than the four well-known swarm optimization algorithms. i CSPM2's performance was also compared to two powerful discrete optimization algorithms (GAIbH [7] and MASC [8]) using a set of Taillard's benchmark instances for the permutation flow shop scheduling problem. The results indicate that i CSPM2 performs better than GAIbH and MASC. The source code of i CSPM2 is publicly available at https://github.com/bilalh2021/iCSPM2

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“…To overcome this, a modified version of PSO is proposed. In PSLPSO, a polynomial self-adaptation inspired by a polynomial mutation operator [27] is employed to improve the performance, which is used to extend the exploration capability of the swarm. Also, to be precise, the value of self-learning rate P sl adapts the diversity of particles.…”

Section: Polynomial Self Learning Psomentioning

confidence: 99%

A Novel Framework for Credit Card Fraud Detection

Mniai,

Tarik,

Jebari

2023

IEEE Access

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Credit card transactions have grown considerably in the last few years. However, this increase has led to significant financial losses around the world. More than that, processing the enormous amount of generated data becomes very challenging, making the datasets highly dimensional and unbalanced. This means the collected data is suffering from two major problems. It is characterized by a severe difference in observation frequency between fraud and non-fraud transactions, and it contains irrelevant, inappropriate, and correlated data that negatively affects their prediction performance. Consequently, it has attracted the interest of machine learning (ML), which has become a significant actor in fraud detection. ML has provided methods such as Logistic Regression (LR), Support vector machines (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN). However, these methods cannot meet the outstanding performance required to detect and predict unusual fraud patterns. In this regard, the contribution of this paper is to propose a framework for fraud detection (FFD). At first, to overcome the unbalanced data problem, the framework uses an undersampling technique. Next, a feature selection (FS) mechanism is applied to select only relevant features. Then, a Support Vector Data Description (SVDD) is used to build the ML model. SVDD aims to create a tight boundary around regular data points to distinguish them from potential outliers or anomalies. In order to enhance optimization capability for its hyperparameters C and σ, a modified version of the Particle Swarm Optimization (PSO) algorithm, Polynomial Self Learning PSO (PSLPSO), is proposed. As a result, the framework's effectiveness is shown in the experimental results on a real credit card transaction dataset.

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Hybridizing Harmony Search algorithm with different mutation operators for continuous problems

Cited by 33 publications

References 20 publications

[Retracted] Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve‐Point Economic Load Dispatch

[Retracted] Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve‐Point Economic Load Dispatch

Island-based Cuckoo Search with Elite Opposition-based Learning and Multiple Mutation Methods for Solving Discrete and Continuous Optimization Problems

A Novel Framework for Credit Card Fraud Detection

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