MORKO: A Multi-objective Runge–Kutta Optimizer for Multi-domain Optimization Problems

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 8, 2025

Abstract In the current landscape, there is a rapid increase in creation of new algorithms designed for specialized problem scenarios. The performance these unfamiliar or practical settings often remains untested. This paper presents development, multi-objective Runge–Kutta optimizer (MORKO), which built upon principles elitist non-dominated sorting and crowding distance. goal to achieve superior efficiency, diversity, robustness solutions. MORKO effectiveness further enhanced by incorporating various strategies that maintain balance between diversity execution efficiency. approach not only directs search toward optimal regions but also ensures process does become stagnant. efficiency compared against renowned like marine predicator algorithm (MOMPA), gradient-based (MOGBO), evolutionary based on decomposition (MOEA/D), genetic (NSGA-II) several test benchmarks such as ZDT, DTLZ, constraint (CONSTR, TNK, SRN, BNH, OSY KITA) real-world engineering design (brushless DC wheel motor, safety isolating transformer, helical spring, two-bar truss, welded beam, disk brake, tool spindle cantilever beam) problems. We used unique, non-overlapping metrics this comparison suggested fresh correlation analysis technique exploration. outcomes were rigorously tested confirmed using non-parametric statistical evaluations. proves excel deriving comprehensive varied solutions many tests challenges, owing its multifaceted features. Looking ahead, has potential applications complex management tasks.

Язык: Английский

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Leadership succession inspired adaptive operator selection mechanism for multi-objective optimization

Mathematics and Computers in Simulation, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 20, 2025

This research utilizes time series models to forecast electricity generation from renewable energy sources and consumption. The configuration of optimal parameters for these typically requires optimization algorithms, but conventional algorithms may struggle with fixed search patterns limited robustness. To address this, we propose an auto-evolution hyper-heuristic algorithm named AE-GAPB. AE-GAPB integrates a genetic (GA) at the high-level component employs particle swarm (PSO) bat (BA) low-level component. GA continuously finds best hyperparameters PSO BA based on prediction accuracy, which significantly accelerates improves accuracy. Additionally, crossover mutation rates evolve over iteration fitness value space, further enhancing its adaptability. We validated six forecasting compared it five well-known as well GAPB without As result, achieved excellent results consumption datasets Hokkaido, Kyushu, Tohoku regions Japan.

Язык: Английский

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Intelligent path planning for autonomous ground vehicles in dynamic environments utilizing adaptive Neuro-Fuzzy control

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 144, С. 110119 - 110119

Опубликована: Янв. 28, 2025

Язык: Английский

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Autonomous obstacle avoidance decision method for spherical underwater robot based on brain-inspired spiking neural network

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127021 - 127021

Опубликована: Фев. 1, 2025

Язык: Английский

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Enhancing Solid Oxide Fuel Cell Efficiency Through Advanced Model Identification Using Differential Evolutionary Mutation Fennec Fox Algorithm

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Фев. 24, 2025

Fuel cells (FCs) are increasingly attracting attention for their efficient conversion of chemical energy into electricity without the need combustion. Their high efficiency and versatility make them a promising technology across various applications. Researchers actively exploring ways to optimize FC systems meet specific needs. Among different types fuel cells, solid oxide (SOFCs) stand out as clean that generates through electrochemical reactions. However, accurately modeling SOFCs, which is essential reducing design costs, presents challenge due complex nonlinear characteristics. An ideal model should be adaptable varying operating pressures temperatures. This research introduces novel approach optimal SOFC identification using differential evolutionary mutation Fennec fox algorithm (DEMFFA). A real-world case study demonstrates superior effectiveness DEMFFA compared existing methods. Additionally, sensitivity analysis evaluates influence temperature pressure on model, with results indicating proposed method achieves higher than other approaches. The sum square error 1.18E-11 followed by parent algorithm, (FFA) (1.24E-09), some algorithms. computational time 1.001 s, FFA (1.199 s) offers significant potential, enhancing renewable energy, minimizing SOFC's environmental impact, improving applications like distributed power generation hydrogen integration.

Язык: Английский

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Enhanced classification of web services using hybrid meta-heuristic algorithms and deep learning

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127281 - 127281

Опубликована: Март 1, 2025

Язык: Английский

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Energy-constrained collaborative path planning for heterogeneous amphibious unmanned surface vehicles in obstacle-cluttered environments

Ocean Engineering, Год журнала: 2025, Номер 330, С. 121241 - 121241

Опубликована: Апрель 19, 2025

Язык: Английский

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Comparative analysis of accuracy and computational complexity across 21 swarm intelligence algorithms

Evolutionary Intelligence, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 5, 2024

Abstract Nonlinear, complex optimization problems are prevalent in many scientific and engineering fields. Traditional algorithms often struggle with these due to their high dimensionality intricate nature, making them time-consuming. Many researchers have proposed new metaheuristic inspired by biological behaviors which comparatively show higher performance accuracy than traditional algorithms. Nature-inspired algorithms, particularly those based on swarm intelligence, offer adaptable efficient solutions challenges. In recent years, intelligence made significant advancements. Classical CEC benchmark suits immersively useful for studying the of According our literature survey, we identified that were evaluated accuracy. Currently, used applications, efficiency computational complexity need be evaluated. A broad-level study popular has not been done recently. Therefore this comprehensively evaluate compare 21 bio-inspired eight non-separable unimodal, separable five multimodal, seven multimodal functions, two 2018 objective functions. We structure mathematical model selected Then categorized into six different behavioral groups. calculated root mean square error between expected actual values. performed an RMSE cross-validation statistical test understand how accurately algorithm resolves average problem. found Artificial Lizard Search Optimization (ALSO) is most prominent efficiency. Besides that, Cat Swarm (CSO), Squirrel Algorithm (SSA), Chimp (CHOA-B) also considered more universal The (SSA) ALSO’s second-best time complexity. Wasp (WSO), Bat-Inspired (BA) presented lowest Finally, several important issues research directions discussed.

Язык: Английский

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Effective Stemmers Using Trie Data Structure for Enhanced Processing of Gujarati Text

International Journal of Computational Intelligence Systems, Год журнала: 2024, Номер 17(1)

Опубликована: Дек. 19, 2024

Язык: Английский

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