Baş, Emine
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Baş, Emine Bas, Emine
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Email Address
ebas@ktun.edu.tr
Main Affiliation
10.02. Department of Software Engineering
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Current Staff
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Research Topics
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Physical Sciences
Fields
Computer ScienceEngineering
Subfields
Artificial IntelligenceIndustrial and Manufacturing EngineeringComputational Theory and MathematicsComputer Networks and Communications
Specific Research Areas
Metaheuristic Optimization Algorithms Research
Evolutionary Algorithms and Applications
Vehicle Routing Optimization Methods
Advanced Multi-Objective Optimization Algorithms
Optimization and Search Problems
Sustainable Development Goals
1NO POVERTY
0
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2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
1
Research Products
4QUALITY EDUCATION
0
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
1
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
0
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
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14LIFE BELOW WATER
0
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15LIFE ON LAND
1
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
0
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Documents
18
Citations
293
h-index
9
Documents
0
Citations
0
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Selçuk University | 21 |
| Konya Technical University | 20 |
| Pamukkale University | 1 |
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Data obtained from OpenAlex
Scholarly Output
39
Articles
26
Views / Downloads
92/220
Supervised MSc Theses
3
Supervised PhD Theses
1
WoS Citation Count
215
Scopus Citation Count
278
Patents
0
Projects
0
WoS Citations per Publication
5.51
Scopus Citations per Publication
7.13
Open Access Source
23
Supervised Theses
4
| Journal | Count |
|---|---|
| Neural Computing and Applications | 4 |
| ARTIFICIAL INTELLIGENCE REVIEW | 3 |
| Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi | 2 |
| Sakarya University Journal of Science | 2 |
| Sinop Üniversitesi Fen Bilimleri Dergisi | 2 |
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39 results
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Now showing 1 - 10 of 39
Article Citation - Scopus: 1The Binary Crayfish Optimization Algorithm With Bitwise Operator and Repair Method for 0–1 Knapsack Problems: an Improved Model(Springer Science and Business Media Deutschland GmbH, 2024-12-26) Bas, E.; Guner, L.B.In this study, Crayfish Optimization Algorithm (COA) was examined. COA, which simulates crayfish’s summer resort behavior, competition behavior, and foraging behavior. COA is a successful heuristic algorithm originally proposed for continuous optimization problems. In this study, the continuous search space of COA was converted into a binary search space with eight different S- and V-shaped transfer functions. Thus, the Binary COA (BinCOA) algorithm was proposed to the literature. The success of BinCOA variations was analyzed on 25 different knapsack problems of different sizes. The most successful transfer function was determined as BinCOAV1. Since the success of BinCOAV1 fell behind many binary heuristic algorithms in the literature, BinCOA was developed with two different methods (bitwise operator and repair method). Thus, the Improved BinCOA (IBinCOA_RX) algorithm was proposed in this study. BinCOA’s local search ability and discovery ability in the binary search space have been improved. The resulting improved BinCOA variations (IBinCOAX (BinCOA with bitwise operator), IBinCOAR (BinCOA with repair method), and IBinCOA_RX (BinCOA with bitwise operator and repair method)) were analyzed in detail and the effect of each method added to BinCOA was detailed in the paper. The success of IBinCOA_RX has been proven by comparing it with eight different binary heuristic algorithms selected from the literature. According to the results, the IBinCOA_RX algorithm showed preferable success for binary optimization problems. In addition, in this study, the effectiveness of BinCOAV1 and IBinCOAX algorithms is also shown on a different binary problem, namely the uncapacitated facility layout problem (UFLP). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.Article Citation - WoS: 13Citation - Scopus: 16A New Binary Coati Optimization Algorithm for Binary Optimization Problems(Springer Science and Business Media Deutschland GmbH, 2023-11-24) Yildizdan, G.; Baş, EmineThe coati optimization algorithm (COA) is a recently proposed heuristic algorithm. The COA algorithm, which solved the continuous optimization problems in its original paper, has been converted to a binary optimization solution by using transfer functions in this paper. Thus, binary COA (BinCOA) is proposed for the first time in this study. In this study, twenty transfer functions are used (four S-shaped, four V-shaped, four Z-shaped, four U-shaped, and four taper-shaped transfer functions). Thus, twenty variations of BinCOA are obtained, and the effect of each transfer function on BinCOA is examined in detail. The knapsack problem (KP) and uncapacitated facility location problem (UFLP), which are popular binary optimization problems in the literature, are chosen to test the success of BinCOA. In this study, small-, middle-, and large-scale KP and UFLP datasets are selected. Real-world problems are not always low-dimensional. Although a binary algorithm sometimes shows superior success in low dimensions, it cannot maintain the same success in large dimensions. Therefore, the success of BinCOA has been tested and demonstrated not only in low-dimensional binary optimization problems, but also in large-scale optimization problems. The most successful transfer function is T3 for KPs and T20 for UFLPs. This showed that S-shaped and taper-shaped transfer functions obtained better results than others. After determining the most successful transfer function for each problem, the enhanced BinCOA (EBinCOA) is proposed to increase the success of BinCOA. Two methods are used in the development of BinCOA. These are the repair method and the XOR gate method. The repair method repairs unsuitable solutions in the population in a way that competes with other solutions. The XOR gate is one of the most preferred methods in the literature when producing binary solutions and supports diversity. In tests, EBinCOA has achieved better results than BinCOA. The added methods have proven successful on BinCOA. In recent years, the newly proposed evolutionary mating algorithm, fire hawk optimizer, honey badger algorithm, mountain gazelle optimizer, and aquila optimizer have been converted to binary using the most successful transfer function selected for KP and UFLP. BinCOA and EBinCOA have been compared with these binary heuristic algorithms and literature. In this way, their success has been demonstrated. According to the results, it has been seen that EBinCOA is a successful and preferable algorithm for binary optimization problems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Article Citation - WoS: 1Citation - Scopus: 8Comparison Between Ssa and Sso Algorithm Inspired in the Behavior of the Social Spider for Constrained Optimization(SPRINGER, 2021-07-08) Baş, Emine; Ülker, ErkanThe heuristic algorithms are often used to find solutions to real complex world problems. In this paper, the Social Spider Algorithm (SSA) and Social Spider Optimization (SSO) which are heuristic algorithms created upon spider behaviors are considered. Performances of both algorithms are compared with each other from six different items. These are; fitness values of spider population which are obtained in different dimensions, number of candidate solution obtained in each iteration, the best value of candidate solutions obtained in each iteration, the worst value of candidate solutions obtained in each iteration, average fitness value of candidate solutions obtained in each iteration and running time of each iteration. Obtained results of SSA and SSO are applied to the Wilcoxon signed-rank test. Various unimodal, multimodal, and hybrid standard benchmark functions are studied to compare each other with the performance of SSO and SSA. Using these benchmark functions, performances of SSO and SSA are compared with well-known evolutionary and recently developed methods in the literature. Obtained results show that both heuristic algorithms have advantages to another from different aspects. Also, according to other algorithms have good performance.Article Citation - WoS: 2Citation - Scopus: 6A New Binary Arithmetic Optimization Algorithm for Uncapacitated Facility Location Problem(Springer Science and Business Media Deutschland GmbH, 2023-12-10) Baş, Emine; Yildizdan, G.Arithmetic Optimization Algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed Algorithm (TSA), and Gray Wolf Optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Article Citation - WoS: 20Citation - Scopus: 24A Binary Social Spider Algorithm for Continuous Optimization Task(SPRINGER, 2020-01-30) Baş, Emine; Ülker, ErkanThe social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors. The original study of this algorithm was proposed to solve continuous problems. In this paper, the binary version of SSA (binary SSA) is introduced to solve binary problems. Currently, there is insufficient focus on the binary version of SSA in the literature. The main part of the binary version is at the transfer function. The transfer function is responsible for mapping continuous search space to discrete search space. In this study, four of the transfer functions divided into two families, S-shaped and V-shaped, are evaluated. Thus, four different variations of binary SSA are formed as binary SSA-Tanh, binary SSA-Sigm, binary SSA-MSigm and binary SSA-Arctan. Two different techniques (SimSSA and LogicSSA) are developed at the candidate solution production schema in binary SSA. SimSSA is used to measure similarities between two binary solutions. With SimSSA, binary SSA's ability to discover new points in search space has been increased. Thus, binary SSA is able to find global optimum instead of local optimums. LogicSSA which is inspired by the logic gates and a popular method in recent years has been used to avoid local minima traps. By these two techniques, the exploration and exploitation capabilities of binary SSA in the binary search space are improved. Eighteen unimodal and multimodal standard benchmark optimization functions are employed to evaluate variations of binary SSA. To select the best variations of binary SSA, a comparative study is presented. The Wilcoxon signed-rank test has applied to the experimental results of variations of binary SSA. Compared to well-known evolutionary and recently developed methods in the literature, the variations of binary SSA performance is quite good. In particular, binary SSA-Tanh and binary SSA-Arctan variations of binary SSA showed superior performance.Article Binary Honey Badger Algorithm Enhanced with Time-Varying Sigmoid Transfer Function and Crossover Strategy(2025-04-24) Emine, Baş; Yıldızdan, GülnurBal porsuklarının yiyecek arama davranışını modelleyen Bal Porsuğu Algoritması (HBA), yakın zamanda önerilen bir meta-sezgisel algoritmadır. Bu çalışmada, sürekli optimizasyon problemlerinin çözümü için önerilen bu algoritmanın ikili versiyonu geliştirildi. Sürekli algoritmayı ikili bir algoritmaya dönüştürmek için S-şekilli transfer fonksiyonu ve çaprazlama stratejisi kullanıldı. Sabit ve zamanla değişen özelliklere sahip sekiz adet S-şekilli transfer fonksiyonu kullanıldı ve en başarılı fonksiyon belirlendi. Ayrıca zamanla değişen transfer fonksiyonlarının etkisi de incelendi. Çaprazlama stratejisi olarak tek nokta, iki nokta ve tekdüze olmak üzere üç strateji uygulandı ve diğerlerinden daha başarılı olan tekdüze stratejisi algoritmaya entegre edildi. Bu şekilde geliştirilen ikili algoritma (BinHBA), on beşi küçük ölçekli ve on ikisi büyük ölçekli olmak üzere toplam yirmi yedi sırt çantası problemi üzerinde test edildi. Sonuçları analiz etmek ve mevcut literatürde bulunan yöntemlerle karşılaştırmak için istatistiksel testler kullanıldı. Sonuçlar, ikili optimizasyon problemleri için önerilen BinHBA'nın etkili ve tercih edilebilir olduğunu gösterdi.Article Improved Social Spider Algorithm for Minimizing Molecular Potential Energy Function(2020-09-03) Baş, Emine; Ülker, Erkan; Emine, BaşThe social spider algorithm (SSA) is a new heuristic algorithm created on spider behaviors to solve continuous optimization problems. In this study, SSA is used in order to minimize a simplified model of the energy function of the molecule. The Molecular potential energy function problem is one of the most important real-life problems. The Molecular potential energy function problem attempts to predict the 3D structure of a protein. SSA is developed by various techniques (Crossover-mutation and Gbest convergence-silent spider techniques) and SSA is called Improved SSA (ISSA). By these techniques, the exploration and exploitation capabilities of SSA in the continuous search space are improved. The general performances of SSA and ISSA are tested on low-scaled and large-scaled thirteen benchmark functions and obtained results are compared with each other. Wilcoxon signed-rank test is applied to SSA and ISSA results. Then, the general performance of the SSA and ISSA is tested on a simplified model of the molecule for different dimensions. Also, the performance of the ISSA is compared to various state-of-art algorithms in the literature. The results showed the superiority of the performance of ISSA.Conference Object Snake Optimizer for Large-Scale Optimizaton Problems(2023) Baş, EmineThe Snake Optimizer (SO) is a newly proposed heuristic algorithm in recent years. It was proposed in the original paper for continuous optimization problems. When the literature was reviewed, it was noticed that the success of SO for large-sized problems was not tested. In this study, the success of SO was examined on data sets consisting of six different large-sized (1024, 3072, and 4868) EEG signals, known as the big data optimization problem. The success of SO has been thoroughly investigated on a big data optimization problem in three different iterations (100, 300, and 500) and three different population sizes (30, 50, and 100). The convergence graphs of the problem datasets according to the population size were drawn and examined. SO was run independently twenty times for each dataset. Statistical evaluations such as average, standard deviation, best, worst, and time were made on the results obtained. According to the average results, the population size and the maximum number of iterations have a direct effect on the result, but they also increase the solution time of the problem. SO has been compared with various heuristic algorithms selected from the literature (Jaya, AOA, BA, PSO-Q, and IPSO-Q). According to the results, SO achieved better results in all big data optimization problems. The results showed that the SO heuristic algorithm was able to maintain its success as the size of the problem increased. This comes from SO's ability to explore locally and globally. According to the results, SO is a heuristic algorithm with strong exploration and exploitation capabilities and can be chosen as an alternative algorithm for large-size continuous optimization problems.Conference Object Business Strategy and Market-Based Performance(2023) Baş, EmineMarket orientation, which is one of the most remarkable orientations; It is the whole of organizational activities aimed at understanding and satisfying the general demands and needs of customers and providing unique customer value. However, in a rapidly changing competitive environment, there is a need for competitive tactics that will strengthen the market orientation and directly contribute to performance, rather than focusing only on market orientation. In this context, the relationships between the components of market orientation, differentiation strategy and firm performance are of great importance.Article An Example of Classification Using a Neural Network Trained by the Zebra Optimization Algorithm(2024-12-29) Emine, Baş; Baş, ŞabanYapay zeka teknikleri eğitim, hesaplama ve tahmin yeteneklerine sahip geniş bir araştırma alanıdır. Bu teknikler arasında yapay sinir ağları (YSA) tahmin modeli olarak yaygın olarak kullanılmaktadır. YSA sınıflandırıcılarındaki öğrenme algoritmaları YSA'nın başarısı üzerinde büyük önem taşımaktadır. YSA modeli genellikle gradyan tabanlı öğrenme modellerini kullanır. Ancak yerel aramada gradyan tabanlı öğrenme modellerinin dezavantajları nedeniyle son yıllarda yerini sezgisel tabanlı algoritmalar almaya başlamıştır. Sezgisel algoritmalar problem çözmedeki başarılarından dolayı son yıllarda birçok araştırmacının dikkatini çekmiştir. Bu çalışmada YSA ağlarının eğitimi için son dönemde önerilen Zebra Optimizasyon Algoritması (ZOA) incelenmiştir. Bu çalışmanın temel amacı sinir ağını ZOA kullanarak eğitmek ve algılayıcı sinir ağının duyarlılığını arttırmaktır. Bu çalışmada ZOA ile entegre yeni bir YSA ağı önerilmektedir. Bu çalışmada ZOA'ya temel oluşturan popülasyon büyüklüğü ve maksimum nesil sayısı parametre ayarlarının YSA ağı üzerindeki etkisini göstermek amacıyla detaylı bir parametre analizi yapılmıştır. Daha sonra YSA ağları için önemli olan katman sayısı, nöron sayısı ve çağ değerleri için parametre analizi yapılmıştır. Böyle ideal bir YSA ağı belirlendi. Bu ideal YSA modeli yedi farklı veri seti üzerinde çalıştırılmış ve doğru verileri tahmin etmede başarılı olmuştur. Ayrıca literatürden seçilen üç farklı sezgisel algoritma (Ceylan Optimizasyon Algoritması (GOA), Çayır Köpekleri Optimizasyonu (PDO), and Balıkkartalı Optimizasyon Algoritması (OOA)) aynı YSA modeli üzerine entegre edilmiş ve benzer koşullar altında çalışan ZOA ile entegre edilmiş YSA'nın sonuçları ile karşılaştırılmıştır. Sonuçlar, önerilen algoritmanın diğer algoritmalara göre sinir ağı katsayısı ile daha fazla yakınsamaya yol açtığını ortaya koymaktadır. Ayrıca önerilen yöntem sinir ağındaki tahmin hatasının azalmasına neden olmuştur.