In this article, we propose the definition of specific mutation operators for testing Geographic Information Systems. We describe the process for applying the operators and generating mutants, and present a case study where these mutation operators a...
Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization probl...
This study investigates and compares different molecular mutation strategies to optimize their application as genetic algorithm operators in drug design. We evaluated five distinct mutation methods—Graph-Based Genetic Algorithm, Graph-Based Gen...
The Grouping Genetic Algorithm (GGA) is an extension to the standard Genetic Algorithm that uses a group-based representation scheme and variation operators that work at the group-level. This metaheuristic is one of the most used to solve combinatori...
Grouping Genetic Algorithms (GGAs) are among the most outstanding methods for solving NP-hard combinatorial optimization problems by efficiently grouping sets of items. Their performance depends on problem-specific heuristics and a balance between ex...
Prolonging the network lifetime is one of the fundamental requirements in wireless sensor networks (WSNs). Sensor node clustering is a very popular energy conservation strategy in WSNs, allowing to achieve energy efficiency, low latency, and scalabil...
The salp swarm algorithm imitates the swarm behavior of salps during navigation and hunting that has been proven the superiority of search for best solution. However, although it has sufficient global search ability, it is still worth paying attentio...
This paper presents a novel algorithm named Five-element Cycle Integrated Mutation Optimization (FECOIMO) for solving the Traveling Thief Problem (TTP). The algorithm introduces a five-element cycle structure that integrates various mutation operatio...
In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid...
Background: Triple-negative breast cancer (TNBC) is an aggressive subtype lacking estrogen, progesterone, and HER2 receptors, and is associated with poor prognosis and limited targeted therapeutic options. TP53 mutations occur in the majority of TNBC...
During the contribution of a metaheuristic algorithm for solving complex problems, one of the major challenges is to obtain the one that provides a well-balanced exploration and exploitation. Among the possible solutions to overcome this issue is to...
We introduce BERT mutation, a novel, domain-independent mutation operator for Genetic Programming (GP) that leverages advanced Natural Language Processing (NLP) techniques to improve convergence, particularly using the Masked Language Modeling approa...
The brain storm optimization (BSO) algorithm has received increased attention in the field of evolutionary computation. While BSO has been applied in numerous industrial scenarios due to its effectiveness and accessibility, there are few theoretical...
To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm...
In this study, we address the integrated scheduling problem involving quay cranes and IGVs in automated terminals. We construct a mixed-integer planning model with the aim of minimizing the total energy consumption during quay crane and IGV operation...
The pathology of Charcot-Marie-Tooth (CMT), a disease arising from mutations in different genes, has been associated with an impairment of mitochondrial dynamics and axonal biology of mitochondria. Mutations in ganglioside-induced differentiation-ass...
The input layer, hidden layer, and output layer are three models of the neural processors that make up feedforward neural networks (FNNs). Evolutionary algorithms have been extensively employed in training FNNs, which can correctly actualize any fini...
The paper proposes a lossless quantum image encryption scheme based on substitution tables (S-box) scrambling, mutation operation and general Arnold transform with keys. First, the key generator builds upon the foundation of SHA-256 hash with plain-i...
Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is wide...
The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems...
Efficient scheduling in materials manufacturing environment plays a crucial role in minimizing idle time, increasing throughput, and making better use of limited resources. This paper introduces a Genetic Algorithm enhanced with Guided Mutation (GA-G...
In order to improve the product disassembly efficiency, the disassembly line balancing problem (DLBP) is transformed into a problem of searching for the optimum path in the directed and weighted graph by constructing the disassembly hierarchy informa...
The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV rou...
Recently, various metaheuristic (MH) optimization algorithms have been presented and applied to solve complex engineering and optimization problems. One main category of MH algorithms is the naturally inspired swarm intelligence (SI) algorithms. SI m...
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...
In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. Krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on...
To address the problem of easily falling into local optimization and low convergence accuracy in the path planning tasks of mobile robots, an Improved Crested Porcupine Optimizer (ICPO) based on chaotic mapping is proposed. The ICPO algorithm employs...
In this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a d...
The automatic train operation (ATO) system of urban rail trains includes a two-layer control structure: upper-layer control and lower-layer control. The upper-layer control is to optimize the target speed curve of ATO, and the lower-layer control is...
An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for system modeling remains complicated. The swarm intelligence algorithms facilitate the IIR filter&rsqu...
Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditio...
The university course scheduling problem (UCSP) is a challenging combinatorial optimization problem that requires optimization of the quality of the schedule and resource utilization while meeting multiple constraints involving courses, teachers, stu...
In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for op...
Time-of-use pricing of electric energy, as an important part of the national policy of energy conservation and emission reduction, requires accurate electric energy data as support. However, due to various reasons, the electric energy data are often...
The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and...
The Zebra Optimization Algorithm (ZOA) is a metaheuristic algorithm inspired by the collective behavior of zebras in the wild. Like many other swarm intelligence algorithms, the ZOA faces several limitations, including slow convergence, susceptibilit...
Background: Understanding PIK3CA mutations and co-mutations in non-small cell lung carcinoma (NSCLC) is critical to developing personalized treatment strategies. Therefore, this study aims to investigate PIK3CA mutations and the accompanying somatic...
The endmember extraction algorithm, which selects a collection of pure signature spectra for different materials, plays an important role in hyperspectral unmixing. In this paper, the endmember extraction algorithm is described as a combinatorial opt...
In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system....
Targeted NGS, widely applied to identify driver oncogenes in advanced lung adenocarcinoma, may also be applied to resected early stage cancers. We investigated resected EGFR-mutated lung adenocarcinoma mutation profiles to evaluate prognostic impacts...
An improved hybrid firefly algorithm with probability attraction model (IHFAPA) is proposed to solve the problems of low computational efficiency and low computational accuracy in solving complex optimization problems. First, the method of square-roo...
To compensate the incapability of traditional cable force adjustment methods to automatically optimize cable forces, this paper proposes Midas/Civil and MATLAB as a structure calculator and a cable force optimizer, and external memory as a data trans...
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. The beta-catenin gene, CTNNB1, is among the most frequently mutated in HCC tissues. However, mutational analysis of HCC tumors is hampered by the difficulty of obta...
When urban rail transit is affected by interference, the fluctuation pattern of passenger flow undergoes mutation, which is not conducive to its operational safety and sustainable development. The more intense the mutation in the passenger flow, the...
To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new popula...
Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problem...
Aiming at the problem that the previous flexible machine network integrated scheduling algorithm only considers positive sequence scheduling, which leads to the extension of product completion time, a reverse-order machine network integrated scheduli...
Evolutionary algorithms solve problems by simulating the evolution of a population of candidate solutions. We focus on evolving permutations for ordering problems such as the traveling salesperson problem (TSP), as well as assignment problems such as...
The DNA damage response (DDR) is critical for maintaining genome stability, and abnormal DDR—resulting from mutations in DNA damage-sensing and repair proteins—is a hallmark of cancer. Here, we aimed to investigate the predictive power of...
Mutations leading to upregulation of efflux pumps can produce multiple drug resistance in the pathogen Pseudomonas aeruginosa. Changes in their DNA binding regions, i.e., palindromic operators, can compromise pump depression and subsequently enhance...
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