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Öğe A binary reptile search algorithm based on transfer functions with a new stochastic repair method for 0-1 knapsack problems(Pergamon-Elsevier Science Ltd, 2023) Ervural, Bilal; Hakli, HuseyinThe Reptile Search Algorithm (RSA), inspired by crocodiles' hunting behavior, is a recently introduced nature -inspired algorithm. Although the original version of the RSA shows outstanding performance in optimizing continuous applications, it is not suitable for discrete optimization problems like 0-1 knapsack problems (0-1 KP). To extend RSA to binary optimization issues, binary RSA (BinRSA) is proposed in this study. A wide range of transfer functions (TFs), including the largely used s-shaped and v-shaped, and recently introduced z-shaped, u -shaped, and taper-shaped, are investigated in the proposed algorithm to map the continuous values into binary. In addition, a novel repair method is introduced to cope with infeasible solutions for 0-1 KP and discussed in detail regarding its efficacy in reaching the optimal solution. The proposed method is validated on three benchmark datasets with 63 instances of 0-1 KP. First, the impact of 25 different transfer functions under six categories on the performance of the proposed binary algorithm is thoroughly investigated, and the results indicate that the taper-shaped T1 transfer function is superior to the other variants of the BinRSA. Then, the effectiveness of the proposed BinRSA with T1 transfer function is compared with some well-known and state-of -art algorithms, including Harris hawks optimization (HHO), slime mould algorithm (SMA), and marine predators algorithm (MPA). The experimental results show that compared to other methods, BinRSA considerably increased the solution accuracy and robustness for solving 0-1 KP.Öğe BinEHO: a new binary variant based on elephant herding optimization algorithm(Springer London Ltd, 2020) Hakli, HuseyinOne of the new optimization techniques proposed in recent years is elephant herding optimization (EHO) algorithm. Despite its short history, EHO has been used to solve many engineering and real-world problems by attracting researcher attention with its advantages such as efficient global search ability, having fewer control parameters and ease of implementation. However, there is no remarkable binary variant of EHO algorithm in the literature. A new binary approach based on EHO algorithm is proposed in this study. The newer binary variant of EHO named as BinEHO is binarized with preserving the search ability of basic EHO. The main purpose of the study is to present a simple, efficient and robust binary variant which copes with different binary problems. Therefore, the proposed method is tested on three important binary optimization problems, 0-1 knapsack, uncapacitated facility location and wind turbine placement, in order to show its performance and accuracy. In addition, the BinEHO is compared with various binary variants on these problems. Experimental results and comparisons show that the BinEHO algorithm is a robust and efficient tool for binary optimization.Öğe Comparing the performances of six nature-inspired algorithms on a real-world discrete optimization problem(Springer, 2022) Hakli, Huseyin; Uguz, Harun; Ortacay, ZeynepMany new, nature-inspired optimization algorithms are proposed these days, and these algorithms are gaining popularity day by day. These algorithms are frequently preferred for these real-world problems as they need less information, are reliable and robust, and have a structure that can easily be applied to discrete problems. Too many algorithms result in difficulty choosing the correct technique for the problem, and selecting an unwise method affects the solution quality. In addition, some algorithms cannot be reliable for some specific real-world problems but very successful for others. In order to guide and give insight into the practitioners and researchers about this problem, studies involving the comparison and evaluation of the performance of algorithms are needed. In this study, the performances of six nature-inspired methods, which included five new implementations of differential evolutionary algorithms (DE), scatter search (SS), equilibrium optimizer (EO), marine predators algorithm (MPA), and honey badger algorithm (HBA) applied to land redistribution problem and genetic algorithms (GA), were compared. In order to compare the algorithms in detail, various performance indicators were used as problem based and algorithm based. Experimental results showed that DE and SS algorithms have a more successful performance than the other methods by solution quality, robustness, and many problem-based indicators.Öğe Genetic algorithm supported by expert system to solve land redistribution problem(Wiley, 2018) Hakli, Huseyin; Uguz, Harun; Cay, TayfunLand redistribution, a real-world optimization problem, involves the distribution of land parcels in predetermined blocks based on the landowners' preferences. This process, measured in weeks or months, is usually performed manually by a technician with the support of computer software. Although various techniques have been developed in recent years to solve this complex problem, they all require improvement. This study aimed to develop a new technique and produce applicable redistribution plans using a genetic algorithm (GA) in combination with an expert system. Blocks of cadastral parcels were determined by a GA using a new objective function to consider the overflow and residual areas as well as the landowners' preferences. The expert system was employed to close (reduce to zero) the overflow or residual areas occurring after the GA distribution. To investigate the performance of the proposed method, the system was used on a real study area and the results were compared against those obtained for the same cadastral situation undertaken by a technician using a similar method from published literature. The experimental results showed that the method proposed in this study performed better than the other methods because it provided a successful and applicable redistribution plan for the study area in a much shorter time.Öğe Implementation of meta-heuristic optimization algorithms for interview problem in land consolidation: A case study in Konya/Turkey(Elsevier Sci Ltd, 2021) Ozsari, Sifa; Uguz, Harun; Hakli, HuseyinThe cultivation of soil for supply of nutritional products necessary for human life is called agriculture. Although agriculture is very important for human beings, it is getting more difficult day by day to cultivate soil efficiently for various reasons. One of the main causes, which significantly prevents sustainable agriculture, is land fragmentation. Land consolidation is one of the important measures taken in order to prevent further fragmentation of agricultural land and the decrease in yield obtained from agriculture. The land consolidation process consists of several time consuming steps. Interview, today conducted manually in Turkey, is the stage where preferences of landowners are taken. These preferences correspond to the blocks that enterprises want their parcels to be placed at the end of consolidation. The interview phase takes a long time as it is carried out manually by a technician. Various studies have been done to improve the land consolidation, but most of these studies focus on other stages of process. In this study, genetic algorithm, particle swarm optimization, non-dominated sorting genetic algorithm II and multi objective particle swarm optimization are applied on the interview problem. The interview problem is a discrete structure optimization problem, thus its solution with traditional methods is difficult and time consuming. Preference lists are generated automatically using optimization algorithms. These lists are compared with the actual interview lists created by the technician. The experimental results confirm the success of algorithms in solving the real world problem.Öğe Implementation of the land reallocation problem using NSGA-II and PESA-II algorithms: a case study in Konya/Turkey(Taylor & Francis Ltd, 2023) Haber, Zeynep; Uguz, Harun; Hakli, HuseyinLand consolidation is one of the essential tools to increase productivity in agricultural production. The most important, complex, and time-consuming step is land allocation among the land consolidation stages. For these reasons, it is inevitable to use computer technology to optimize this process. This study used reallocation models based on PESA-II and NSGA-II optimization algorithms to solve the reallocation problem. The methods were compared with the optimization algorithms in the literature and the conventional method obtained by the technician. The applied algorithms have achieved successful results by parcel number, average parcel size, and reallocation cost.Öğe An improved artificial bee colony algorithm for balancing local and global search behaviors in continuous optimization(Springer Heidelberg, 2020) Hakli, Huseyin; Kiran, Mustafa ServetThe artificial bee colony, ABC for short, algorithm is population-based iterative optimization algorithm proposed for solving the optimization problems with continuously-structured solution space. Although ABC has been equipped with powerful global search capability, this capability can cause poor intensification on found solutions and slow convergence problem. The occurrence of these issues is originated from the search equations proposed for employed and onlooker bees, which only updates one decision variable at each trial. In order to address these drawbacks of the basic ABC algorithm, we introduce six search equations for the algorithm and three of them are used by employed bees and the rest of equations are used by onlooker bees. Moreover, each onlooker agent can modify three dimensions or decision variables of a food source at each attempt, which represents a possible solution for the optimization problems. The proposed variant of ABC algorithm is applied to solve basic, CEC2005, CEC2014 and CEC2015 benchmark functions. The obtained results are compared with results of the state-of-art variants of the basic ABC algorithm, artificial algae algorithm, particle swarm optimization algorithm and its variants, gravitation search algorithm and its variants and etc. Comparisons are conducted for measurement of the solution quality, robustness and convergence characteristics of the algorithms. The obtained results and comparisons show the experimentally validation of the proposed ABC variant and success in solving the continuous optimization problems dealt with the study.Öğe An improved scatter search algorithm for the uncapacitated facility location problem(Pergamon-Elsevier Science Ltd, 2019) Hakli, Huseyin; Ortacay, ZeynepThe uncapacitated facility location (UFL) problem is a NP-hard and pure binary optimization problem. The main goal of UFL is that try to fmd an undetermined number of facilities to minimize the sum of constant setup and serving costs of customers. Nowadays, in solving many NP problems, optimization techniques are preferred instead of conventional ones due to their simple structure, ease of application and acceptable results in reasonable time. In this study, the scatter search algorithm (SS) was improved to solve the UFL problems. The SS method can be applied directly to problems with binary search space and supports random search mechanism with good solutions obtained from previous problem solving efforts as opposed to other evolutionary algorithms. In order to compromise between exploitation and exploration in the improved scatter search (ISS), the global search ability of the basic SS algorithm is enhanced by using different crossover techniques like an ensemble, while the local search ability is improved by mutation operations on the best solutions. To investigate effects of the improvements and to show its performance, the ISS is compared with the twelve different methods found in the literature for solving the 15 UFL problems in the OR-Lib dataset. The experimental results show that the proposed method obtained the optimum value for 13 of the 15 problems and had a superior performance compared to other techniques considering the solution quality and robustness. The ISS is also compared with a technique using the local search method on the OR-Lib and a different dataset named M*. When all experimental results are evaluated, it is seen that the proposed method is an effective, robust and successful tool for solving the UFL problems.Öğe Modeling of reallocation in land consolidation with a hybrid method(Elsevier Sci Ltd, 2018) Ertunc, Ela; Cay, Tayfun; Hakli, HuseyinLand consolidation is one of the important tool of increasing productivity in agricultural production. Land consolidation not only consolidates fragmented land, but also improves the standards of landowners in agriculture, technical, social and cultural areas. Land consolidation projects consist of various stages. The most important, complicated and time-consuming part of these stages is land reallocation. Land reallocation is a process which requires a long time and high operating costs and in which there frequently arise disputes between landowners. For these reasons, it is inevitable to use computer technology to optimize this process. In this study, a hybrid method including genetic algorithm and fuzzy logic techniques which enable reallocation to be done automatically in land consolidation has been used. The crossover rate and the operation of the genetic algorithm (GA) method have been realized as a self-adaptive structure using fuzzy logic techniques A similar study used for the land reallocation problem in the literature, the results of reallocation plans obtained by the technician and the results obtained by the hybrid method have been compared. When the experimental results are evaluated, it has been found that the hybrid method used is more successful and efficient than similar studies in the literature and also has a better reallocation plan.Öğe A new approach for wind turbine placement problem using modified differential evolution algorithm(Tubitak Scientific & Technological Research Council Turkey, 2019) Hakli, HuseyinEnergy use is increasing worldwide with industrialization and advancing technology. Following this increase, renewable energy resources are increasingly preferred to reduce the costs of energy production. Wind energy is preferred as a renewable energy resource because it is clean and safe. Wind turbines are used to meet the demand for wind energy. They are placed close to each other to generate higher amounts of energy. However, the wake effect problem arises in these types of layouts, and this hinders the turbines from producing the desired yield. A modified differential evolution (MDE) algorithm was proposed in this study to solve the placement problem for wind turbines, and employed a binary-real-coded method - obtained by combining binary coding and real coding. The proposed method contains three different modifications: generation of the initial population, regeneration, and mutation. The effective distribution of the wind turbines on land was achieved with a preliminary operation proposed to generate the initial population. In addition, with the MDE method, population regeneration and elitism were carried out to increase the diversity of population and to preserve the success of the method. Finally, a mutation operation was performed on the individuals to alternate the presence or absence of wind turbines. To investigate the performance of the MDE method in solving the wind turbine placement problem, the method was applied to a study area of 2 x 2 km. The results were compared with those obtained with other methods used in the published literature for the wind turbine placement problem. The most successful and productive placement was achieved using the proposed method, and experimental results showed that the MDE is an efficient and successful tool to solve the wind turbine placement problem.Öğe The optimization of wind turbine placement using a binary artificial bee colony algorithm with multi-dimensional updates(Elsevier Science Sa, 2023) Hakli, HuseyinOptimal placement of wind turbines on wind farms by considering cost and total power is an important optimization problem. This study uses an artificial bee colony (ABC) algorithm to solve the wind turbine placement problem. The ABC, performing on continuous search space, is adapted to address problems with a binary structure through a proposed simple and effective approach. Because a single-dimensional update for binary form could weaken the ABC algorithm's population diversity and exploration ability, a multi-dimensional update is performed in the proposed method. Multi-dimensional updates are first applied to employed bee and onlooker bee phases of the ABC algorithm separately and together for different dimensions and then analyzed on a 2 kmx2 km wind farm area divided into a 10x10 grid. The wind turbine placement problem is also applied for a 20x20 grid structure that makes more flexible placement by dividing the wind area into more sections. The proposed method has a better or comparable level of performance in both grid structures and is an effective method for wind turbine placement. Moreover, the proposed method is compared with different binary ABC variants to show and validate its performance, and it performs better than these variants by solution quality and robustness.Öğe A Performance Evaluation and Two New Implementations of Evolutionary Algorithms for Land Partitioning Problem(Springer Heidelberg, 2020) Hakli, HuseyinMany bio-inspired techniques are proposed and implemented to solve real-world applications. The number of these techniques is increasing day by day, so the researchers (especially out of computer sciences) have difficulty in deciding which technique to select for the problem. In this study, two new implementations to solve land partitioning problem and also a performance analysis of three evolutionary algorithms were carried out on this real-world engineering problem. Land partitioning is a discrete optimization problem that cannot be solved in linear time with conventional techniques. Two new implementations of automated land partitioning (ALP-DE and ALP-SS) were carried out by using differential evolution algorithm (DE) and scatter search (SS) methods. The algorithms were adapted to the land partitioning problem by being discretized with permutation coding. These two proposed methods were compared with a similar study in the published literature and a designer's plan for a project area that contains 18 blocks using a mathematical model. These proposed automatic methods (ALP-DE and ALP-SS) resulted in more successful and more appropriate partitioning plans than those of a designer in accordance with land partitioning criteria. When the comparison of these three different evolutionary algorithms was examined, the ALP-SS method showed superior performance in all blocks. The low standard deviation values of the proposed methods indicated that both methods are robust and successful tools for the land partitioning problem.Öğe A Qualified Search Strategy with Artificial Bee Colony Algorithm for Continuous Optimization(Springer Heidelberg, 2020) Hakli, HuseyinOne of the most popular population-based and swarm intelligence algorithms is the artificial bee colony. Although the ABC method is known for its efficiency in exploration, it has a poor performance in exploitation ability. It uses a single solution search equation that does not provide a balance between exploration and intensification adequately, and this is one of the most common problems in optimization techniques. This study proposes an artificial bee colony algorithm with a qualified search strategy (QSSABC) that uses four different solution search equations to deal with these problems. In order to increase the ability of exploitation, the QSSABC uses the global best solution of population in both of these equations. Equations in the QSSABC method are selected by roulette-wheel method based on their success rates, and equation with the lowest success rate within determined periods is eliminated. The equations' success rates are reset at the end of each period, and it is expected that equations will prove their success again in every period. This qualified search strategy ensures an efficient use of number of function evaluations, and also it achieves balance between global and local search. To evaluate accuracy and performance of the QSSABC, twenty-eight classical functions, twenty-four CEC05 functions and thirty CEC14 functions were used. Simulation results showed that the QSSABC surpasses other methods such as distABC, MABC, ABCVSS in classical functions, and that it is a successful tool for problems with different characteristics by showing better performance over other methods for CEC05 and CEC14 test functions.Öğe A tree-seed algorithm based on intelligent search mechanisms for continuous optimization(Elsevier, 2021) Kiran, Mustafa Servet; Hakli, HuseyinOne of the recently proposed metaheuristic algorithms is tree-seed algorithm, TSA for short. TSA is developed by inspiring the relation between trees and their seeds in order to solve continuous optimization problems, and it has a simple but effective algorithmic structure. The algorithm uses two different solution generating mechanisms in order to improve balance local and global search abilities. However, when the algorithm is analyzed in detail, it is seen that there are some issues in the basic algorithm. These are (i) when trees in the stand approaches to each other, the diversification in the stand is lost, (ii) there is no mechanism to get rid of local minima for a tree, (iii) some of the fitness calculation goes to waste due to seed generation mechanism of basic TSA. In order to address these issues, four different approaches (withering process, sequential seed generation, best-based solution update rule and dimensional selection for the solution update rule) have been proposed for the basic TSA, and all these approaches have been also integrated within algorithmic framework of TSA, named new tree-seed algorithm briefly NTSA, and each of them has been used to solve 28 CEC2013 benchmark functions. In the experimental comparisons, the variants of TSA have been compared with each other, and the better algorithm, NTSA, has been compared with 17 state-of-art algorithms such as artificial bee colony, particle swarm optimization, differential evolution, genetic algorithm, covariance matrix adaptation evolutionary strategy etc. The experimental analysis and comparisons show that the NTSA shows better or similar performance than/with the compared algorithms in terms of solution quality and robustness. (C) 2020 Elsevier B.V. All rights reserved.
