Search Results (2,274)

Conventional deep learning models rely heavily on the McCulloch–Pitts (MCP) neuron, limiting their interpretability and biological plausibility. The Dendritic Neuron Model (DNM) offers a more realistic alternative by simulating nonlinear and compartmentalized processing within dendritic branches, enabling efficient and transparent learning. While DNMs have shown strong performance in various tasks, their learning capacity at the single-neuron level remains underexplored. This paper proposes a Reinforced Dynamic-grouping Differential Evolution (RDE) algorithm to enhance synaptic plasticity within the DNM framework. RDE introduces a biologically inspired mutation-selection strategy and an adaptive grouping mechanism that promotes effective exploration and convergence. Experimental evaluations on benchmark classification tasks demonstrate that the proposed method outperforms conventional differential evolution and other evolutionary learning approaches in terms of accuracy, generalization, and convergence speed. Specifically, the RDE-DNM achieves up to 92.9% accuracy on the BreastEW dataset and 98.08% on the Moons dataset, with consistently low standard deviations across 30 trials, indicating strong robustness and generalization. Beyond technical performance, the proposed model supports societal applications requiring trustworthy AI, such as interpretable medical diagnostics, financial screening, and low-energy embedded systems. The results highlight the potential of RDE-driven DNMs as a compact and interpretable alternative to traditional deep models, offering new insights into biologically plausible single-neuron computation for next-generation AI. Full article

As an evolutionary advancement to conventional synthetic aperture radar (SAR), passive bistatic SAR (PBSAR) utilizing geostationary orbit (GEO) satellite signals demonstrates significant potential for high-resolution imaging. However, PBSAR faces dual challenges in computational efficiency and phase error compensation. Traditional accelerated back-projection (BP) variants developed from monostatic SAR are incompatible with PBSAR’s geometry, and autofocus BP (AFBP) methods exhibit prohibitive computational costs and inadequate space-variant phase error handling. This study first develops a bistatic ground Cartesian back-projection (BGCBP) algorithm through subimage wavenumber spectrum correction, specifically adapted to GEO-satellite-based PBSAR. Compared to conventional BP, the BGCBP achieves an order-of-magnitude complexity reduction without resolution degradation. Building upon this foundation, we propose a subimage autofocus BGCBP (SIAF-BGCBP) methodology, synergistically integrating autofocus processing with BGCBP’s accelerated framework. SIAF-BGCBP reduces phase estimation’s complexity by 90% through subimage pixel density optimization while maintaining estimation accuracy. Further enhancement of SIAF-BGCBP via geometric inversion would enable the precise compensation of space-variant phase errors while remaining efficient. Simulations and real-environment experiments verify the effectiveness of the proposed methods. Full article

The Bi-directional Evolutionary Structural Optimization (BESO) method, owing to its algorithmic simplicity and strong scalability, has emerged as one of the most prevalent topology optimization methodologies in current research and industrial applications. To overcome the limitations of existing commercial finite element software (e.g., ABAQUS), particularly regarding the closed architecture of topology optimization modules and low efficiency in 3D complex structure optimization, this study proposes an ABAQUS–MATLAB cooperative framework. This innovative approach implements direct read/write operations via Python scripts on CAE/ODB model databases, coupled with MATLAB-based master control programs for sensitivity analysis, mesh filtering, and design variable updating. Compared with conventional integration methods employing INP/FIL file interactions, the proposed framework reduces computational time through MATLAB’s advanced matrix operations while maintaining solution accuracy. Validation cases including 2D cantilever beams and 3D wheel hubs demonstrate the method’s precision and computational efficiency. Practical applications in lightweight design of a hydraulic transmission test bench adapter support achieved 31% volume reduction while satisfying strength and stiffness requirements, significantly lowering material costs. The developed cooperative framework provides an extensible solution for high-efficiency topology optimization of complex engineering structures, balancing algorithmic transparency with practical applicability. Full article

Meta-heuristic algorithms are computational methods inspired by evolutionary processes, animal or plant behaviors, physical events, and other natural phenomena. Due to their success in solving optimization problems, meta-heuristic algorithms are widely used in the literature, leading to the development of novel variants. In this paper, new swarm-based meta-heuristic algorithms, called Improved Roosters Algorithm (IRA), Bipolar Roosters Algorithm (BRA), and Bipolar Improved Roosters Algorithm (BIRA), which are mainly based on Roosters Algorithm (RA), are presented. First, the new versions of RA (IRA, BRA, and BIRA) were compared in terms of performance, revealing that BIRA achieved significantly better results than the other variants. Then, the performance of the BIRA algorithm was compared with the performances of meta-heuristic algorithms widely used in the literature, Standard Genetic Algorithm (SGA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Grey Wolf Optimizer (GWO), and thus, its success in the literature was tested. Moreover, RA was also included in this test to show that the new version, BIRA, is more successful than the previous one (RA). For all comparisons, 20 well-known benchmark optimization functions, 11 CEC2014 test functions, and 17 CEC2018 test functions, which are also in the CEC2020 test suite, were employed. To validate the significance of the results, Friedman and Wilcoxon Signed Rank statistical tests were conducted. In addition, three commonly used problems in the field of engineering were used to test the success of algorithms in real-life scenarios: pressure vessel, gear train, and tension/compression spring design. The results indicate that the proposed algorithm (BIRA) provides better performance compared to the other meta-heuristic algorithms. Full article

The maximum k-coverage problem (MKCP) is a problem of finding a solution that includes the maximum number of covered rows by selecting k columns from an m $\times$n matrix of 0s and 1s. This is an NP-hard problem that is difficult to solve in a realistic time; therefore, it cannot be solved with a general deterministic algorithm. In this study, genetic algorithms (GAs), an evolutionary arithmetic technique, were used to solve the MKCP. Genetic algorithms (GAs) are meta-heuristic algorithms that create an initial solution group, select two parent solutions from the solution group, apply crossover and repair operations, and replace the generated offspring with the previous parent solution to move to the next generation. Here, to solve the MKCP with binary and integer encoding, genetic algorithms were designed with various crossover and repair operators, and the results of the proposed algorithms were demonstrated using benchmark data from the OR-library. The performances of the GAs with various crossover and repair operators were also compared for each encoding type through experiments. In binary encoding, the combination of uniform crossover and random repair improved the average objective value by up to 3.24% compared to one-point crossover and random repair across the tested instances. The conservative repair method was not suitable for binary encoding compared to the random repair method. In contrast, in integer encoding, the combination of uniform crossover and conservative repair achieved up to 4.47% better average performance than one-point crossover and conservative repair. The conservative repair method was less suitable with one-point crossover operators than the random repair method, but, with uniform crossover, was better. Full article

As a commonly used method in classification, feature selection can be treated as a bi-objective optimization problem, whose objectives are to minimize both the classification error and the number of selected features, suitable for multi-objective evolutionary algorithms (MOEAs) to tackle. However, due to the discrete optimization environment and the increasing number of features, traditional MOEAs could face shortcomings in searching abilities, especially for large-scale or high-dimensional datasets. Thereby, in this work, an adaptive initialization and reproduction-based evolutionary algorithm (abbreviated as AIR) is proposed, specifically designed for addressing bi-objective feature selection in classification. In AIR, an adaptive initialization mechanism (abbreviated as AI) and an adaptive reproduction method (abbreviated as AR) have been both designed by analyzing the characteristics of currently selected solutions in order to improve their search abilities and balance the convergence and diversity performances. Moreover, the designing of adaptive initialization also utilizes the implicit symmetry of solutions generated around some interpolation axes in the objective space. In the experiments, AIR is comprehensively compared with five state-of-the-art MOEAs in a list of 20 real-life classification datasets, with its the statistical performance being overall the best in terms of several indicators. Full article

When addressing constrained multi-objective optimization problems (CMOPs), the key challenge lies in achieving a balance between the objective functions and the constraint conditions. However, existing evolutionary algorithms exhibit certain limitations when tackling CMOPs with complex feasible regions. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm based on a dual-population cooperative correlation (CMOEA-DCC). Under the CMOEA-DDC framework, the system maintains two independently evolving populations: the driving population and the conventional population. These two populations share information through a collaborative interaction mechanism, where the driving population focuses on objective optimization, while the conventional population balances both objectives and constraints. To further enhance the performance of the algorithm, a shift-based density estimation (SDE) method is introduced to maintain the diversity of solutions in the driving population, while a multi-criteria evaluation metric is adopted to improve the feasibility quality of solutions in the normal population. CMOEA-DDC was compared with seven representative constrained multi-objective evolutionary algorithms (CMOEAs) across various test problems and real-world application scenarios. Through an in-depth analysis of a series of experimental results, it can be concluded that CMOEA-DDC significantly outperforms the other competing algorithms in terms of performance. Full article

In the context of expensive constraint multi-objective problems, it is evident that the feasible domain shapes and sizes of different problems vary considerably. The difficulty in finding optimal solutions presents a significant challenge in ensuring the surrogate-assisted evolutionary algorithm’s feasibility, convergence, and diversity. To more effectively address the distinctive characteristics of the feasible domain and objective function across a range of problems, we have developed a Kriging-based surrogate-assisted evolutionary algorithm tailored to the current population’s preferences. The algorithm can optimize the population according to the current population’s requirements. Additionally, considering the varying degrees of accuracy observed in the surrogate models at different stages, this paper employs a dynamic approach to the number of surrogate model evaluations, contingent on the accuracy of the current surrogate model. Two types of Pareto frontier search are distinguished: unconstrained and constrained. Moreover, distinct fill sampling strategies are devised in accordance with the specific optimization requirements of the current population. After assessing the proposed solutions, the discrepancy between the actual fitness value and the surrogate model’s prediction is calculated.The discrepancy is used to modify the number of evaluations conducted on the surrogate model. In order to illustrate the algorithm’s efficacy, it is benchmarked against the current state-of-the-art algorithms on various test problems. The experimental results demonstrate that the proposed algorithm performs better than other advanced methods. Full article

Combining energy storage with base-load power sources offers an effective way to cover the fluctuation of renewable energy. This study proposes a nuclear–solar hybrid energy system (NSHES), which integrates a small modular thorium molten salt reactor (smTMSR), concentrating solar power (CSP), and thermal energy storage (TES). Two operation modes are designed and analyzed: constant nuclear power (mode 1) and adjusted nuclear power (mode 2). The nondominated sorting genetic algorithm II (NSGA-II) is applied to minimize both the deficiency of power supply probability (DPSP) and the levelized cost of energy (LCOE). The decision variables used are the solar multiple (SM) of CSP and the theoretical storage duration (TSD) of TES. The criteria importance through inter-criteria correlation (CRITIC) method and the technique for order preference by similarity to ideal solution (TOPSIS) are utilized to derive the optimal compromise solution. The electricity curtailment probability (ECP) is calculated, and the results show that mode 2 has a lower ECP compared with mode 1. Furthermore, the configuration with an installed capacity of nuclear and CSP (100:100) has the lowest LCOE and ECP when the DPSP is satisfied with certain conditions. Optimizing the NSHES offers an effective approach to mitigating the mismatch between energy supply and demand. Full article

The design of differential evolution (DE) operators has long been a key topic in the research of metaheuristic algorithms. This paper systematically reviews the functional differences between mechanism improvements and operator improvements in terms of exploration and exploitation capabilities, based on the general patterns of algorithm enhancements. It proposes a theoretical hypothesis: operator improvement is more directly associated with the enhancement of an algorithm’s exploitation capability. Accordingly, this paper designs a new differential operator, DE/current-to-pbest/order, based on the classic DE/current-to-pbest/1 operator. This new operator introduces a directional judgment mechanism and a replacement strategy based on individual fitness, ensuring that the differential vector consistently points toward better individuals. This enhancement improves the effectiveness of the search direction and significantly strengthens the algorithm’s ability to delve into high-quality solution regions. To verify the effectiveness and generality of the proposed operator, it is embedded into two mainstream evolutionary algorithm frameworks, JADE and LSHADE, to construct OJADE and OLSHADE. A systematic evaluation is conducted using two authoritative benchmark sets: CEC2017 and CEC2011. The CEC2017 set focuses on assessing the optimization capability of theoretical complex functions, covering problems of various dimensions and types; the CEC2011 set, on the other hand, targets multimodal and hybrid optimization challenges in real engineering contexts, featuring higher structural complexity and generalization requirements. On both benchmark sets, OLSHADE demonstrates outstanding solution quality, convergence efficiency, and result stability, showing particular advantages in high-dimensional complex problems, thus fully validating the effectiveness of the proposed operator in enhancing exploitation capability. In addition, the operator has a lightweight structure and is easy to integrate, with good portability and scalability. It can be embedded as a general-purpose module into more DE variants and EAs in the future, providing flexible support for further performance optimization in solving complex problems. Full article

The Internet Shopping Optimization Problem with multiple item Units looks for the best selection of stores where to buy various or individual units in a required list of items to minimize the final purchase cost. The problem belongs to the most challenging complexity class of optimization problems (NP-Hard). This paper adds to the already complex problem a more difficult situation with a second objective conflicting with the purchase cost minimization. As far as we know, this is the first state-of-the-art proposal with conflicting objectives for the Internet Shopping Optimization Problem or its variants. The objective in conflict with the minimization of the purchase final cost is the cash-back or reward points on personal or corporate credit cards, the most common payment method for online purchases. Due to the nature of the conflicting objectives, this paper proposes using evolutionary multi-objective optimization algorithms. We perform an experimental comparison using eight algorithms from the literature. The experimental results show that NSGA-II achieves the best overall performance for the studied instances from the state of the art. Full article

This article studies the effective design of content distribution networks over cloud computing platforms. This problem is relevant nowadays to provide fast and reliable access to content on the internet. A bio-inspired evolutionary multiobjective optimization approach is applied as a viable alternative to solve realistic problem instances where exact optimization methods are not applicable. Ad hoc representation and search operators are applied to optimize relevant metrics from the point of view of both system administrators and users. In the evaluation of problem instances built using real data, the evolutionary multiobjective optimization approach was able to compute more accurate solutions in terms of cost and quality of service when compared to the exact resolution method. The obtained results represent an improvement over greedy heuristics from 47.6% to 93.3% in terms of cost while maintaining competitive quality of service. In addition, the computed solutions had different tradeoffs between the problem objectives. This can provide different options for content distribution network design, allowing for a fast configuration that fulfills specific quality of service demands. Full article

Some complex social networks are driven by adaptive and co-evolutionary patterns. However, these can be difficult to detect and analyse since the links between actors are circumstantial and often not revealed. This paper employs a Geographic Information Systems (GIS) integrated multi-agent-based approach to simulate co-evolution in a complex social network. A case study is proposed for the modelling of contractual relationships between road freight transport companies. The model employs empirical data from a survey of transport companies located in the Basque Country (Spain) and utilises the DBSCAN community detection algorithm to simulate the effect of cluster size in the network. Additionally, a local spatial association indicator is employed to identify potentially favourable environments. The model enables the evolution of the network, leading to more complex collaborative structures. By means of iterative simulations, the study demonstrates how collaborative networks self-organise by distributing activity and knowledge and evolving into complex polarised systems. Furthermore, the simulations with different minimum cluster sizes indicate that clusters benefit the agents that are part of them, although they are not a determining factor in the network participation of other non-clustered agents. Full article

This article aims to review the industrial applications of AI-based intelligent system algorithms in the manufacturing sector to find the latest methods used for sustainability and optimisation. In contrast to previous review articles that broadly summarised existing methods, this paper specifically emphasises the most recent techniques, providing a systematic and structured evaluation of their practical applications within the sector. The primary objective of this study is to review the applications of intelligent system algorithms, including metaheuristics, evolutionary algorithms, and learning-based methods within the manufacturing sector, particularly through the lens of optimisation of workflow in the production lines, specifically Job Shop Scheduling Problems (JSSPs). It critically evaluates various algorithms for solving JSSPs, with a particular focus on Flexible Job Shop Scheduling Problems (FJSPs), a more advanced form of JSSPs. The manufacturing process consists of several intricate operations that must be meticulously planned and scheduled to be executed effectively. In this regard, Production scheduling aims to find the best possible schedule to maximise one or more performance parameters. An integral part of production scheduling is JSSP in both traditional and smart manufacturing; however, this research focuses on this concept in general, which pertains to industrial system scheduling and concerns the aim of maximising operational efficiency by reducing production time and costs. A common feature among research studies on optimisation is the lack of consistent and more effective solution algorithms that minimise time and energy consumption, thus accelerating optimisation with minimal resources. Full article

The study addresses the impact of selected environmental factors on the energy production of photovoltaic systems under real outdoor conditions, with particular emphasis on the application of evolutionary computation techniques. The experiment was carried out on a dedicated test stand, where measurements were made under natural environmental conditions. Parameters such as solar irradiance, wind speed, temperature, air pollution, and obtained PV power were continuously recorded. Initial correlation analysis using Pearson and Spearman coefficients confirmed associations between environmental factors and power output, especially solar irradiance. In order to advance the analysis beyond conventional methods, a linear regression model was developed in which the model weights were optimized using evolutionary algorithms, allowing for a more robust assessment of the contribution of each parameter. The results showed that solar irradiance accounted for 97.79% of the variance in photovoltaic power, while temperature (0.95%), air pollution (0.72%), and wind speed (0.54%) had significantly lower impacts. The implementation of evolutionary algorithms represents a novel approach in this context and has proven to be effective in quantifying environmental influence under complex real-world conditions. Furthermore, the findings highlight the indirect role of air pollution in attenuating irradiance and reducing system efficiency. These insights provide a foundation for the development of adaptive control strategies and predictive models to optimize the performance of the photovoltaic system in dynamic environmental settings. Full article