Tree seed algorithm (TSA) is a recently proposed metaheuristic algorithm for solving continuous optimization problems. In order to use TSA in binary optimization problems, the SimLogicTSA method was ...developed by adding logic gates and Jaccard’s similarity measure to this algorithm by Cinar and Kiran. Although SimLogicTSA is generally successful in small, medium, and large size problems, it has not been successful in the huge-sized problems by stucking into local minima. To overcome this problem, a new local search mechanism called enhanced local search module (ELSM) is proposed and the SimLogicTSA-ELSM algorithm is suggested by implementing the ELSM mechanism to the original SimLogicTSA algorithm. The proposed ELSM mechanism consists of a swap operator and logic-based gates. To analyze the contribution of the ELSM mechanism to the algorithm, firstly, the original SimLogicTSA and SimLogicTSA-ELSM algorithms were compared on the Cap and M* problem sets. The obtained results showed that the proposed algorithm produced more successful results than the original SimLogicTSA. Then, the proposed SimLogicTSA-ELSM is compared with many state-of-art algorithms in the literature by using different performance metrics on Cap and M* problem sets. The results show that SimLogicTSA-ELSM outperforms the compared algorithms in nearly all cases. Especially, the performance of the SimLogicTSA-ELSM stands out in huge-sized problems.
•A novel binary local search module named as ELSM has been proposed.•The proposed ELSM has been implemented on binary Tree seed algorithm.•The proposed algorithm has been applied on Cap and M* UFL problems.•The performance of the proposed algorithm has been tested with different metrics.•Performance comparison of the proposed algorithm and state-of-art algorithms is done.
In order to efficiently solve the binary optimization problems by using differential evolution (DE), a class of new transfer functions, Taper-shaped transfer function, is firstly proposed by using ...power functions. Then, the novel binary differential evolution algorithm based on Taper-shaped transfer functions (T-NBDE) is proposed. T-NBDE transforms a real vector representing the individual encoding into a binary vector by using the Taper-shaped transfer function, which is suitable for solving binary optimization problems. For verifying the practicability of Taper-shaped transfer functions and the excellent performance of T-NBDE, T-NBDE is firstly compared with binary DE based on S-shaped, U-shaped and V-shaped transfer functions, respectively. Subsequently, it is compared with the state-of-the-art algorithms for solving the knapsack problem with a single continuous variable (KPC) and the uncapacitated facility location problem (UFLP). The comparison results show that Taper-shaped transfer functions are competitive than existing transfer functions, and T-NBDE is more effective than existing algorithms for solving KPC problem and UFLP problem.
•This paper proposes a new approach to solve uncapacited facility location problem.•The performance of basic scatter search algorithm is enhanced using improvements.•Proposed method is compared with ...twelve different methods found in the literature.•ISS is an effective, robust and successful tool for solving the UFL problems.
The uncapacitated facility location (UFL) problem is a NP-hard and pure binary optimization problem. The main goal of UFL is that try to find 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.
•This paper introduces an ABC variant to solve binary optimization problems.•The performance of the proposed method is investigated on well-known UFLPs.•The proposed method is compared with the ABC ...variants and PSO variants.•The experimental results show that the proposed algorithm is an alternative tool for binary optimization.
Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABCbin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABCbin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABCbin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABCbin is an alternative and simple binary optimization tool in terms of solution quality and robustness.
► Introducing a new optimization algorithm for pure binary optimization field. ► Introducing a new binary version of the artificial bee colony algorithm. ► Investigating the application of the ...algorithm on the well-established uncapacitated facility location problem. ► The algorithm is capable to find the global optimum of all investigated problems. ► The algorithm behaves constantly and performs reliably.
Artificial bee colony (
ABC) algorithm is one of the recently proposed swarm intelligence based algorithms for continuous optimization. Therefore it is not possible to use the original
ABC algorithm directly to optimize binary structured problems. In this paper we introduce a new version of
ABC, called
DisABC, which is particularly designed for binary optimization.
DisABC uses a new differential expression, which employs a measure of dissimilarity between binary vectors in place of the vector subtraction operator typically used in the original
ABC algorithm. Such an expression helps to maintain the major characteristics of the original one and is respondent to the structure of binary optimization problems, too. Similar to original
ABC algorithm,
DisABC's differential expression works in continuous space while its consequence is used in a two-phase heuristic to construct a complete solution in binary space. Effectiveness of
DisABC algorithm is tested on solving the uncapacitated facility location problem (
UFLP). A set of 15 benchmark test problem instances of
UFLP are adopted from OR-Library and solved by the proposed algorithm. Results are compared with two other state of the art binary optimization algorithms, i.e.,
binDE and
PSO algorithms, in terms of three quality indices. Comparisons indicate that
DisABC performs very well and can be regarded as a promising method for solving wide class of binary optimization problems.
Jaya, yakın zamanda sürekli optimizasyon problemlerinin çözümü için önerilen popülasyon tabanlı metasezgisel bir algoritmadır. Literatürde ikili optimizasyon problemlerinin çözümü için çeşitli Jaya ...varyantları geliştirilmiştir. Bunlardan biri olan JayaX-LSM algoritması CAP problemlerinin çözümünde kullanılmış ve başarılı sonuçlar üretmiştir. Ancak CAP problemlerinden daha yüksek boyutlu ve kompleks bir yapıya sahip olan M* problemleri üzerinde test ettiğimizde algoritmanın oldukça başarısız sonuçlar ürettiği görülmüştür. Bu çalışmada, ikili optimizasyon problemlerinde çözüm uzayının etkili bir şekilde aranmasını sağlayan yeni bir yerel arama modülü (ELSM) geliştirilmiştir. Bu modül ikili JayaX algoritmasına eklenerek JayaX-ELSM algoritması önerilmiştir. Önerilen JayaX-ELSM algoritmasının performansı öncelikle JayaX-LSM algoritmasıyla CAP ve M* problem setleri üzerinde karşılaştırmalı olarak analiz edilmiştir. Daha sonra, önerilen algoritma, literatürde yakın zamanda yayınlanmış toplam 11 farklı algoritmayla performans karşılaştırmasına tabi tutulmuştur. Elde edilen sonuçlar, önerilen JayaX-ELSM'nin JayaX-LSM algoritmasının CAP problemlerinde sergilediği performansı devam ettirdiğini, M* problemlerinde de JayaX-LSM'den çok daha başarılı sonuçlar ürettiğini göstermektedir. Ayrıca önerilen algoritmanın M* problemleri üzerindeki performansının, diğer algoritmalarla karşılaştırıldığında rekabetçi ve ümit verici olduğu gözlenmiştir.
The uncapacitated facility location problem (UFLP) is a popular combinatorial optimization problem with practical applications in different areas, from logistics to telecommunication networks. While ...most of the existing work in the literature focuses on minimizing total cost for the deterministic version of the problem, some degree of uncertainty (e.g., in the customers' demands or in the service costs) should be expected in real-life applications. Accordingly, this paper proposes a simheuristic algorithm for solving the stochastic UFLP (SUFLP), where optimization goals other than the minimum expected cost can be considered. The development of this simheuristic is structured in three stages: (i) first, an extremely fast savings-based heuristic is introduced; (ii) next, the heuristic is integrated into a metaheuristic framework, and the resulting algorithm is tested against the optimal values for the UFLP; and (iii) finally, the algorithm is extended by integrating it with simulation techniques, and the resulting simheuristic is employed to solve the SUFLP. Some numerical experiments contribute to illustrate the potential uses of each of these solving methods, depending on the version of the problem (deterministic or stochastic) as well as on whether or not a real-time solution is required.
The uncapacitated facility location problem (UFLP) is a popular NP‐hard optimization problem that has been traditionally applied to logistics and supply networks, where decisions are difficult to ...reverse. However, over the years, many new application domains have emerged, in which real‐time optimization is needed, such as Internet of Vehicles (IoV), virtual network functions placement, and network controller placement. IoV scenarios take into account the presence of multiple roadside units (RSUs) that should be frequently assigned to operating vehicles. To ensure the desired quality of service level, the allocation process needs to be carried out frequently and efficiently, as vehicles' demands change. In this dynamic environment, the mapping of vehicles to RSUs needs to be reoptimized periodically over time. Thus, this article proposes an agile optimization algorithm, which is tested using existing benchmark instances. The experiments show that it can efficiently generate high‐quality and real‐time results in dynamic IoV scenarios.
The present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the ...well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to minimize the total cost, which is the sum of facility opening costs and transportation costs. Since UFLP is a binary optimization problem, FPA, which is introduced to solve real-valued optimization problems, is redesigned to be able to conduct search in binary domains. This constitutes one of the contributions of the present study. In this context, some evolutionary operators such as crossover and mutation are adopted by the proposed FPA. Next, the mutation operator is further enhanced by making use of an adaptive procedure that introduces greater level of diversity at earlier iterations and encourages intensification toward the end of search. Thus, while premature convergence and local optima problems at earlier iterations are avoided, a more intensified search around the found promising regions is performed. Secondarily, as demonstrated in this study, by making use of the reported evolutionary procedures, FPA is able to run in binary spaces without employing any additional auxiliary procedures such as transfer functions. All available benchmarking instances are solved by the proposed approach. As demonstrated by the comprehensive experimental study that includes statistically verified results, the developed approach is found as a promising algorithm that can be extended to numerous binary optimization problems.
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.
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