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    Analysis, test and management of the metaheuristic searching process: an experimental study on SOS
    (2020) Kahraman, Hamdi Tolga; Aras, Sefa; Sönmez, Yusuf; Güvenç, Uğur; Gedikli, Eyüp
    In a search process, getting trapped in a local minimum or jumping the global minimum problems are also one of the biggestproblems of meta-heuristic algorithms as in artificial intelligence methods. In this paper, causes of these problems are investigatedand novel solution methods are developed. For this purpose, a novel framework has been developed to test and analyze the metaheuristic algorithms. Additionally, analysis and test studies have been carried out for Symbiotic Organisms Search (SOS)Algorithm. The aim of the study is to measure the mimicking a natural ecosystem success of symbiotic operators. Thus, problemsin the search process have been discovered and operators' design mistakes have been revealed as a case study of the developedtesting and analyzing method. Moreover, ways of realizing a precise neighborhood search (intensification) and getting rid of thelocal minimum (increasing diversification) have been explored. Important information that enhances the performance of operatorsin the search process has been achieved through experimental studies. Additionally, it is expected that the new experimental testmethods developed and presented in this paper contributes to meta-heuristic algorithms studies for designing and testing.
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    Development of a Levy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems
    (Springer, 2021) Duman, Serhat; Kahraman, Hamdi T.; Guvenc, Ugur; Aras, Sefa
    This article presents an improved version of the coyote optimization algorithm (COA) that is more compatible with nature. In the proposed algorithm, fitness-distance balance (FDB) and Levy flight were used to determine the social tendency of coyote packs and to develop a more effective model imitating the birth of new coyotes. The balanced search performance, global exploration capability, and local exploitation ability of the COA algorithm were enhanced, and the premature convergence problem resolved using these two methods. The performance of the proposed Levy roulette FDB-COA (LRFDBCOA) was compared with 28 other meta-heuristic search (MHS) algorithms to verify its effectiveness on 90 benchmark test functions in different dimensions. The proposed LRFDBCOA and the COA ranked, respectively, the first and the ninth, according to nonparametric statistical results. The proposed algorithm was applied to solve the AC optimal power flow (ACOPF) problem incorporating thermal, wind, and combined solar-small hydro powered energy systems. This problem is described as a constrained, nonconvex, and complex power system optimization problem. The simulation results showed that the proposed algorithm exhibited a definite superiority over both the constrained and highly complex real-world engineering ACOPF problem and the unconstrained convex/nonconvex benchmark problems.
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    Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination
    (Springer, 2021) Kahraman, Hamdi Tolga; Bakir, Huseyin; Duman, Serhat; Kati, Mehmet; Aras, Sefa; Guvenc, Ugur
    This article has four main objectives. These are: to develop the dynamic fitness-distance balance (dFDB) selection method for meta-heuristic search algorithms, to develop a strong optimization algorithm using the dFDB method, to create an optimization model of the coordination of directional overcurrent relays (DOCRs) problem, and to optimize the DOCRs problem using the developed algorithm, respectively. A comprehensive experimental study was conducted to analyze the performance of the developed dFDB selection method and to evaluate the optimization results of the DOCRs problem. Experimental studies were carried out in two steps. In the first step, to test the performance of the developed dFDB method and optimization algorithm, studies were conducted on three different benchmark test suites consisting of different problem types and dimensions. The data obtained from the experimental studies were analyzed using non-parametric statistical methods and the most effective among the developed optimization algorithms was determined. In the second step, the DOCRs problem was optimized using the developed algorithm. The performance of the proposed method for the solution to the DOCRs coordination problem was evaluated on five test systems including the IEEE 3-bus, the IEEE 4-bus, the 8-bus, the 9-bus, and the IEEE 30-bus test systems. The numerical results of the developed algorithm were compared with previously proposed algorithms available in the literature. Simulation results showed the effectiveness of the proposed method in minimizing the relay operating time for the optimal coordination of DOCRs.
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    Exploring the Effect of Distribution Methods on Meta-Heuristic Searching process
    (Ieee, 2017) Kahraman, Hamdi Tolga; Aras, Sefa; Güvenç, Uğur; Sönmez, Yusuf
    In this study, the effect of distributions of solution candidates on the problem space in the meta-heuristic search process and the performance of algorithms has been investigated. For this purpose, solution candidates have been created with random and gauss (normal) distributions. Search performance is measured separately for both types of distribution of algorithms. The performances of the algorithms have been tested on the most popular and widely used benchmark problems. Experimental studies have been conducted on the most recent meta-heuristic search algorithms. It has been seen that the search performance of algorithms varies considerably depending on the method of distribution. In fact, better results were obtained than the distribution methods used in the original versions of the algorithms. Algorithms have revealed their abilities in terms of neighborhoods searching, getting rid of local minimum traps and speeding up searches.
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    Fitness-Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources
    (Elsevier, 2021) Guvenc, Ugur; Duman, Serhat; Kahraman, Hamdi Tolga; Aras, Sefa; Kati, Mehmet
    One of the most difficult types of problems computationally is the security-constrained optimal power flow (SCOPF), a non-convex, nonlinear, large-scale, nondeterministic polynomial time optimization problem. With the use of renewable energy sources in the SCOPF process, the uncertainties of operating conditions and stress on power systems have increased even more. Thus, finding a feasible solution for the problem has become a still greater challenge. Even modern powerful optimization algorithms have been unable to find realistic solutions for the problem. In order to solve this kind of difficult problem, an optimization algorithm needs to have an unusual exploration ability as well as exploitation-exploration balance. In this study, we have presented an optimization model of the SCOPF problem involving wind and solar energy systems. This model has one problem space and innumerable local solution traps, plus a high level of complexity and discrete and continuous variables. To enable the optimization model to find the solution effectively, the adaptive guided differential evolution (AGDE) algorithm was improved by using the Fitness-Distance Balance (FDB) method with its balanced searching and high-powered diversity abilities. By using the FDB method, solution candidates guiding the search process in the AGDE algorithm could be selected more effectively as in nature. In this way, AGDE's exploration and balanced search capabilities were improved. To solve the SCOPF problem involving wind and solar energy systems, the developed algorithm was tested on an IEEE 30-bus test system under different operational conditionals. The simulation results obtained from the proposed algorithm were effective in finding the optimal solution compared to the results of the metaheuristics algorithms and reported in the literature. (C) 2021 Elsevier B.V. All rights reserved.
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    Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem
    (Taylor & Francis Ltd, 2022) Sönmez, Yusuf; Duman, Serhat; Kahraman, Hamdi T.; Kati, Mehmet; Aras, Sefa; Güvenç, Uğur
    The Transient Stability Constrained Optimal Power Flow (TSCOPF) has become an important tool for power systems today. TSCOPF is a nonlinear optimisation problem, making its solution difficult, especially for small power systems. This paper presents a new optimisation method that incorporates Fitness-Distance Balance (FDB) with the Artificial Ecosystem Optimisation (AEO) algorithm to improve the solution quality in multi-dimensional and nonlinear optimisation problems. The proposed method, named the Fitness-Distance Balance Artificial Ecosystem Optimisation (FDBAEO), also has the capacity to solve the TSCOPF problem efficiently. In order to evaluate the proposed algorithm, it was tested on IEEE CEC benchmarks and on an IEEE 30-bus test system for the TSCOPF problem. Simulation results were compared with the basic AEO algorithm and other current meta-heuristic methods reported in the literature. The results showed that the proposed method was more effective in converging at the global optimum point in solving the TSCOPF problem compared to the other algorithms. This situation indicates that the design changes made in the decomposition phase of the AEO were more suitable for simulating the operation of the algorithm in the real world. The FDBAEO has exhibited a promising performance in solving both single-objective optimisation and constrained real-world engineering design problems.
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    A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems
    (Pergamon-Elsevier Science Ltd, 2022) Duman, Serhat; Kahraman, Hamdi Tolga; Sönmez, Yusuf; Güvenç, Uğur; Kati, Mehmet; Aras, Sefa
    The teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based metaheuristic search algorithm. It combines the exploitation of the teaching learning-based optimization (TLBO) with the exploration of the artificial bee colony (ABC). With the hybridization of these two nature-inspired swarm intelligence algorithms, a robust method has been proposed to solve global optimization problems. However, as with swarm-based algorithms, with the TLABC method, it is a great challenge to effectively simulate the selection process. Fitness-distance balance (FDB) is a powerful recently developed method to effectively imitate the selection process in nature. In this study, the three search phases of the TLABC algorithm were redesigned using the FDB method. In this way, the FDB-TLABC algorithm, which imitates nature more effectively and has a robust search performance, was developed. To investigate the exploitation, exploration, and balanced search capabilities of the proposed algorithm, it was tested on standard and complex benchmark suites (Classic, IEEE CEC 2014, IEEE CEC 2017, and IEEE CEC 2020). In order to verify the performance of the proposed FDB-TLABC for global optimization problems and in the photovoltaic parameter estimation problem (a constrained real-world engineering problem) a very comprehensive and qualified experimental study was carried out according to IEEE CEC standards. Statistical analysis results confirmed that the proposed FDB-TLABC provided the best optimum solution and yielded a superior performance compared to other optimization methods.