Kiziloluk, Soner; Özer, Ahmet Bedri

Researchers have developed different metaheuristic algorithms to solve various optimization problems. The efficiency of a metaheuristic algorithm depends on the balance between exploration and exploitation. This paper presents the hybrid parliamentary optimization and big bang-big crunch (HPO-BBBC) algorithm, which is a combination of the parliamentary optimization algorithm (POA) and the big bang-big crunch (BB-BC) optimization algorithm. The intragroup competition phase of the POA is a process that searches for potential points in the search space, thereby providing an exploration mechanism. By contrast, the BB-BC algorithm has an effective exploitation mechanism. In the proposed method, steps of the BB-BC algorithm are added to the intragroup competition phase of the POA in order to improve the exploitation capabilities of the POA. Thus, the proposed method achieves a good balance between exploration and exploitation. The performance of the HPO-BBBC algorithm was tested using well-known mathematical test functions and compared with that of the POA, the BB-BC algorithm, and some other metaheuristics, namely the genetic algorithm, multiverse optimizer, crow search algorithm, dragonfly algorithm, and moth-flame optimization algorithm. The HPO-BBBC algorithm was found to achieve better optimization performance and a higher convergence speed than the above-mentioned algorithms on most benchmark problems.

AtıfScopus3·WoS1·OpenAlex3

İncele

Kiziloluk, Soner; Ozer, Ahmet Bedri

In recent years, data on the Internet has grown exponentially, attaining enormous dimensions. This situation makes it difficult to obtain useful information from such data. Web mining is the process of using data mining techniques such as association rules, classification, clustering, and statistics to discover and extract information from Web documents. Optimization algorithms play an important role in such techniques. In this work, the parliamentary optimization algorithm (POA), which is one of the latest social-based metaheuristic algorithms, has been adopted for Web page classification. Two different data sets (Course and Student) were selected for experimental evaluation, and HTML tags were used as features. The data sets were tested using different classification algorithms implemented in WEKA, and the results were compared with those of the POA. The POA was found to yield promising results compared to the other algorithms. This study is the first to propose the POA for effective Web page classification.

AtıfScopus11·WoS9·OpenAlex10

İncele

Kiziloluk, Soner; Ozer, Ahmet Bedri

Optimizasyon, bir problemin alternatif çözümleri içinden en uygununu seçme işlemidir. Optimizasyon problemlerinin çözümü için, kabul edilebilir sürede optimuma yakın çözümler verebilen birçok sezgisel optimizasyon algoritması önerilmiştir. Literatürde çok başarılı sezgisel optimizasyon algoritmaları bulunsa da; tüm problemlerin çözümü için en optimum çözümü bulan algoritmalar henüz tasarlanmamıştır. Bu yüzden yeni sezgisel optimizasyon algoritmaları önerilmekte ya da var olanların daha etkili çalışması için öneriler sunulmaktadır

AtıfTR Dizin1

İncele

Kiziloluk, Soner; Özer, Ahmet Bedri

Many of meta-heuristics optimization algorithms which are used to solving optimization problems are inspired from biological evolution, physical, social and chemical processes. Electromagnetism-like Algorithm (EM), Big Bang-Big Crunch Algorithm (BB-BC) and Gravitational Search Algorithm (GSA) are investigated in this study and they are physics-based meta-heuristics techniques. Performances of EM, BB-BC and GSA are compared by using Rastrigin, Rosenbrock and De Jong test functions.

AtıfWoS1

İncele

Kiziloluk, Soner; Alatas, Bilal

In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used for automatically mining of comprehensible and accurate classification rules within datasets which have numerical attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is very new and no applications in complex data mining problems have been performed, the results seem promising. The used objective function is very flexible and many different objectives can easily be added to. The intervals of the numerical attributes in the rules have been automatically found without any a priori process, as done in other classification rules mining algorithms, which causes the modification of datasets.

AtıfWoS12·OpenAlex18

İncele

Alatas, Bilal; Kiziloluk, Soner

Optimization is the process of making something better. For example, in an f(x) function in optimization, it is asked for finding x values which make the values of the result minimum. Different methods have been proposed for solving optimization problems. Some of these processes have been inspired by natural processes. Ant colony optimization algorithm can be given as an example. Some of the other methods have been inspired by social events. Although the number of social based methods is limited, the most known one is tabu search algorithm. Recently, researchers have developed teaching-learning based algorithm, imperialist competitive algorithm, and parliamentary optimization algorithm. In this study, how the imperialist competitive algorithms and parliamentary optimization algorithms which are social-based algorithms are modeled and how parliamentary optimization algorithm is implemented on the problems have been mentioned.

AtıfTR Dizin2

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