The wind farm layout optimization problem (WFLOP) aims to maximize energy utilization efficiency and mitigate losses caused by wake effects optimizing the spatial of turbines. Although Genetic Algorithms (GAs) Particle Swarm Optimization (PSO) have been widely used in WFLOP due their discrete characteristics, they still limitations global exploration capability depth. Meanwhile, Differential Evolution algorithm (DE), known for its strong ability excellent performance handling complex...

This study on soil salinity inversion in coastal tidal flats based Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing flat agriculture. proposes an improved approach using a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy...

Its complexity constrains advancements in fusion energy and high applications driven by plasma physics, multiscale phenomena beyond classical computing limits. These transformative solutions, especially simulations, for which exponential speedup is possible, represent significant promise toward breakthroughs sustainable extreme state studies. In this review, Quantum Computing (QC) explored as a means to drive physics simulations forward providing such high-energy systems. This includes...

The famous McCulloch–Pitts neuron model has been criticized for being overly simplistic in the long term. At same time, dendritic (DNM) shown to be effective prediction problems, and it accounts nonlinear information-processing capacity of synapses dendrites. Furthermore, since classical error back-propagation (BP) algorithm typically experiences problems caused by overabundance saddle points local minima traps, an efficient learning approach DNMs remains desirable but difficult implement....

Neuronal models have remained an important area of research in computer science. The dendritic neuron model (DNM) is a novel neuronal recent years. Previous studies focused on training DNM using more appropriate algorithms. This paper proposes improvement to based the activity excitation and three new models. Each improved are designed mimic inhibition neurons. proposed this shown be effective experimental part. All original their own strengths, so it can considered that well enriches...

Highlights•Population interaction networks for seven MHAs are established.•Seven distribution models used to fit the frequency of degrees nodes.•The CDF node classic PSO conforms Logistic distribution.•The GSA and variants distribution.AbstractIn this paper, a novel study on way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using scheme known as population (PIN). Specifically, three representative MHAs, including differential evolutionary algorithm...

The wind farm layout optimization problem (WFLOP) aims to maximize energy utilization efficiency under different conditions by optimizing the spatial of turbines fully mitigate losses caused wake effects. Some high-performance continuous methods, such as differential evolution (DE) variants, exhibit limited performance when directly applied due WFLOP’s discrete nature. Therefore, metaheuristic algorithms with inherent characteristics like genetic (GAs) and particle swarm (PSO) have been...

Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed these connections, known as complex networks, embody underlying principles governing system’s behavior. While have been previously applied in field evolutionary computation, prior studies limited ability reach conclusive conclusions. Based on our investigations, we are against notion that there is a direct link between network structure algorithm and its...

The Snow Ablation Optimizer (SAO) algorithm represents a new intelligent search method founded on the sublimation and melting phenomena of snow. It incorporates dual-population mechanism to balance exploration exploitation. However, one limitation SAO is its propensity become trapped in local optima. Coincidentally, Differential Evolution (DE) exhibits strong global capabilities. In light this, we propose fusion algorithm, named DESAO, that combines strengths DE. Our experimental results,...

The Gravitational Search Algorithm (GSA) is a Newtonian gravity-based meta-heuristic algorithm. algorithm updates population members' locations according to the gravitational pull between them in order solve optimization issue. However, some cases, convergence of not fast enough. In improve speed while alleviating problem easily falling into local optima, we propose search with hierarchical structure (EHGSA) guided by elite individuals. This can help have ability jump out optimum improving...

In recent years, artificial neural networks (ANNs), which are based on the foundational model established by McCulloch and Pitts in 1943, have been at forefront of computational research. Despite their prominence, ANNs encountered a number challenges, including hyperparameter tuning need for vast datasets. It is because many strategies predominantly focused enhancing depth intricacy these that essence processing capabilities individual neurons occasionally overlooked. Consequently,...

Wind farm layout optimization (WFLO) is focused on utilizing algorithms to devise a more rational turbine layout, ultimately maximizing power generation efficiency. Traditionally, genetic have been frequently employed in WFLO due the inherently discrete nature of problem. However, recent years, researchers shifted towards enhancing continuous and incorporating constraints address challenges. This approach has shown remarkable promise, outperforming traditional gaining traction among...

The complexity and functional evolution of mammalian visual systems have always been a focal point in neuroscience biological science research. primary neurons that output motion direction signals research for nearly 130 years. These are widely present the cortex retina mammals. Although relevant pathways discovered studied almost 60 years due to experimental accessibility, still remains at cellular level. specific functions overall operational mechanisms component direction-selective yet be...

The genetic algorithm is a very successful metaheuristic algorithm. Its wide applications in recommendation algorithms, feature selection, and industrial design have proven its effectiveness. However, it still suffers from the issues of low performance local optima. Therefore, we propose method called Chaotic Mapping Genetic Algorithm with Multiple Strategies (CGA-M) inspired by existence chaos nature's evolution shorten evolutionary process linear decline method. We test effectiveness on...

The spherical evolution algorithm (SE) is a unique proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due challenges arising from multiple choices of operators utilization search method. In this paper, we introduce an enhancement method that incorporates weights individuals’ dimensions affected by individual fitness during iteration process, aiming improve adaptively...

In this paper, a method of improvement with sparrow search algorithm is proposed, namely differential (DSSA). It inspired by some advanced evolution algorithms and algorithm. DSSA, we proposed an operation to improve the ability locally. Several results show that comprehensive performance DSSA improved compared SSA better than classical algorithms.

Sparrow search algorithm (SSA) is a new evolutionary that has the advantage of good exploration ability. Benefiting from its strategy, SSA can effectively alleviate local optima. However, it still suffers issue low solution quality because weak exploitation Therefore, we propose an archive-based differential learning incorporated sparrow (ASSA), which introduces strategy for enhancing ability SSA. The searches superior in neighborhood region optimal each iteration and uses to replace...

Chaotic local search based differential evolutionary optimization algorithm (CJADE) is an improved on JADE. It incorporates the ideas of chaotic mapping and to enhance global capabilities algorithms. Although performance this has been greatly compared with original algorithm, there are still some shortcomings, such as insufficient exploitation ability, slow convergence, exploration easy lead optimum. To solve these problems, we propose a linearly decreasing strategy named L-CJADE, which...

The differential evolution with optional external archive (JADE) algorithm is a competitive improvement based on (DE) algorithm, but it tends to converge local optima. We propose new enhancement JADE, namely HJADE, which incorporates strategy of regulating the population size using hybrid functions strike more favorable balance between exploration and exploitation. used 30 problems from IEEE CEC 2017 benchmark set assess its implementation compare HJADE other JADE-based algorithms...