Gene expression data are usually redundant, and only a subset of them presents distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in bioinformati...
Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors t...
Differential evolution (DE) stands out as a straightforward yet remarkably powerful evolutionary algorithm employed for real-world problem-solving purposes. In the DE algorithm, few parameters are used, and the population is evolved by applying vario...
The objective of this research is to develop metaheuristic methods by using the differential evolution (DE) algorithm for solving the U-shaped assembly line balancing problem Type 1 (UALBP-1). The proposed DE algorithm is applied for balancing the li...
A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented a...
Differential evolution is a popular heuristic black-box numerical optimization algorithm which is often used due to its simplicity and efficiency. Parameter adaptation is one of the main directions of study regarding the differential evolution algori...
Differential evolution is capable of searching for the optimum for different types of optimization problems with a few inputs, which has gained worldwide popularity. In this paper, we propose a parameters adaptation scheme based on the stagnation rat...
Metaheuristic algorithms are techniques that have been successfully applied to solve complex optimization problems in engineering and science. Many metaheuristic approaches, such as Differential Evolution (DE), use the best individual found so far fr...
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time comput...
In recent decades, differential evolution (DE) has been employed to address a diverse range of nonlinear, nondifferentiable, and nonconvex optimization problems. In this study, we introduce an enhanced adaptive differential evolution algorithm to add...
In this study, a new parameter control scheme is proposed for the differential evolution algorithm. The developed linear bias reduction scheme controls the Lehmer mean parameter value depending on the optimization stage, allowing the algorithm to imp...
In this work, the differential evolution algorithm behavior under a fixed point arithmetic is analyzed also using half-precision floating point (FP) numbers of 16 bits, and these last numbers are known as FP16. In this paper, it is considered that it...
In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer str...
Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up s...
Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-...
To address the poor searchability, population diversity, and slow convergence speed of the differential evolution (DE) algorithm in solving capacitated vehicle routing problems (CVRP), a new multistrategy-based differential evolution algorithm with t...
Parameter adaptation is one of the key research fields in the area of evolutionary computation. In this study, the application of neuroevolution of augmented topologies to design efficient parameter adaptation techniques for differential evolution is...
Multi-Robot Task Assignment (MRTA) is a critical and inherently multi-objective problem in diverse real-world applications, demanding the simultaneous optimization of conflicting objectives such as minimizing total travel distance and balancing robot...
Open canals are important water transfer structures used in water resources systems. As such, they may require substantial amount of investment depending on its length and cross section. Therefore, cross section design should be carried out on an opt...
In this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied...
To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differenti...
Differential evolution (DE) is a very effective stochastic optimization algorithm based on population for solving various real-world problems. The quality of solutions to these problems is mainly determined by the combination of mutation strategies a...
This study proposes a dual-population algorithmic scheme for differential evolution and specific mutation strategy. The first population contains the newest individuals, and is continuously updated, whereas the other keeps the top individuals through...
The performance of the differential evolution algorithm (DE) is known to be highly sensitive to the values assigned to its control parameters. While numerous studies of the DE control parameters do exist, these studies have limitations, particularly...
The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution...
The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strateg...
Since different incident waves will cause the same array to perform differently with respect to the wave energy converter (WEC), the parameters of the incident wave, including the incident angle and the incident wave number, are taken into account fo...
Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 kna...
Evolutionary Algorithms (EAs) are those based on the phenomenon of survival of the fittest. Differential Evolution (DE) is a member of this family, and it is well-suited for handling problems with real-valued variables. However, to use DE to solve bi...
Differential evolution (DE) is a stochastic optimization technique that imitates the evolution process in nature. This paper uses an improved adaptive differential evolution to solve gravity inversion with multiplicative regularization. Compared with...
Differential evolution (DE) is a popular and efficient heuristic numerical optimization algorithm that has found many applications in various fields. One of the main disadvantages of DE is its sensitivity to parameter values. In this study, we invest...
A recloser requires a fast operating time in the first shot to optimally clear a temporary fault. The operating time is dependent on the time-dial, the pick-up settings, and the fault current. The recloser detects the fault current from the grid supp...
The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems. Effective constrained optimization methods can be considered in tackling t...
This work aims to obtain the switching angles to reduce the total harmonic distortion (THD) and compare the different sets of angles obtained by modifying the modular index (Mi) in a seven-level multilevel inverter using the differential evolution (D...
In this paper, a hybrid differential evolution and weight total least squares method (HDE-WTLSM) is proposed for antenna array pattern synthesis. A variable diagonal weight matrix is introduced in total least squares method. Then, the weight matrix i...
Differential evolution (DE) is one of the best evolutionary algorithms (EAs). In recent decades, many techniques have been developed to enhance the performance of this algorithm, such as the Improve Self-Adaptive Differential Evolution (ISADE) algori...
In Taiwan, over 45% of the energy in common buildings is used for the air-conditioning system. In particular, the chiller plant consumes about 70% of the energy in air-conditioning system. The electric energy consumption of air-condition system in a...
For numerous years, researchers have extensively explored real parameter single-objective optimization by evolutionary computation. Among the various types of evolutionary algorithms, Differential Evolution (DE) performs outstandingly. Recently, the...
Stochastic simulations are often used to determine the crossover rates and step size of evolutionary algorithms to avoid the tuning process, but they cannot fully utilize information from the evolutionary process. A two-stage adaptive differential ev...
The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particl...
The differential evolution (DE) algorithm is a simple and efficient population-based evolutionary algorithm. In DE, the mutation strategy and the control parameter play important roles in performance enhancement. However, single strategy and fixed pa...
Differential evolution is an evolutionary algorithm that is used to solve complex numerical optimization problems. Differential evolution balances exploration and exploitation to find the best genes for the objective function. However, finding this b...
The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO...
In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability with...
Since the discovery of the Differential Evolution algorithm, new and improved versions have continuously emerged. In this paper, we review selected algorithms based on Differential Evolution that have been proposed in recent years. We examine the mec...
The disposal of infectious waste remains one of the most severe medical, social, and environmental problems in almost every country. Choosing the right location and arranging the most suitable transport route is one of the main issues in managing haz...
This paper describes the development of a methodology for air propeller optimization using Bezier curves to describe blade geometry. The proposed approach allows for more flexibility in setting the propeller shape, for example, using a variable airfo...
Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global o...
Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an import...
Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modification...
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