Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

Cognitive Computation, Journal Year: 2020, Volume and Issue: 12(5), P. 897 - 939

Published: July 5, 2020

Language: Английский

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Chaotic quasi-opposition marine predator algorithm for automatic data clustering

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

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Efficiency Analysis of Binary Metaheuristic Optimization Algorithms for Uncapacitated Facility Location Problems

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112968 - 112968

Published: March 1, 2025

Language: Английский

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Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 160 - 160

Published: March 10, 2025

This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global exploration local exploitation for both unimodal multimodal search spaces. Extensive benchmarking against classical cutting-edge metaheuristics on IEEE CEC2022 functions—encompassing unimodal, multimodal, landscapes—demonstrates achieves highly competitive results in terms of solution accuracy, convergence speed, robustness. Statistical analysis using Wilcoxon sum-rank tests further underscores JADEDO’s consistent advantage over several established optimizers, reflecting its proficiency navigating complex, high-dimensional problems. To validate real-world applicability, was also evaluated three engineering design problems (pressure vessel, spring, speed reducer). Notably, it achieved top-tier or near-optimal designs constrained, high-stakes environments. Moreover, to demonstrate suitability security-oriented tasks, applied an attack-response scenario, efficiently identifying cost-effective, low-risk countermeasures under stringent time constraints. These collective findings highlight as robust, flexible, high-performing framework capable tackling benchmark-oriented practical challenges.

Language: Английский

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Advancing Engineering Solutions with Protozoa-Based Differential Evolution: A Hybrid Optimization Approach

Automation, Journal Year: 2025, Volume and Issue: 6(2), P. 13 - 13

Published: March 28, 2025

This paper presents a novel Hybrid Artificial Protozoa Optimizer with Differential Evolution (HPDE), combining the biologically inspired principles of (APO) powerful optimization strategies (DE) to address complex and engineering design challenges. The HPDE algorithm is designed balance exploration exploitation features, utilizing innovative features such as autotrophic heterotrophic foraging behaviors, dormancy, reproduction processes alongside DE strategy. performance was evaluated on CEC2014 benchmark functions, it compared against two sets state-of-the-art optimizers comprising 23 different algorithms. results demonstrate HPDE’s good performance, outperforming competitors in 24 functions out 30 from first set second set. Additionally, has been successfully applied range problems, including robot gripper optimization, welded beam pressure vessel spring speed reducer cantilever three-bar truss optimization. consistently showcase solving these problems when competing

Language: Английский

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Adaptive Cybersecurity Neural Networks: An Evolutionary Approach for Enhanced Attack Detection and Classification

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(19), P. 9142 - 9142

Published: Oct. 9, 2024

The increasing sophistication and frequency of cyber threats necessitate the development advanced techniques for detecting mitigating attacks. This paper introduces a novel cybersecurity-focused Multi-Layer Perceptron (MLP) trainer that utilizes evolutionary computation methods, specifically tailored to improve training process neural networks in cybersecurity domain. proposed dynamically optimizes MLP’s weights biases, enhancing its accuracy robustness defending against various attack vectors. To evaluate effectiveness, was tested on five widely recognized security-related datasets: NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, CSE-CIC-IDS2018. Its performance compared with several state-of-the-art optimization algorithms, including Cybersecurity Chimp, CPO, ROA, WOA, MFO, WSO, SHIO, ZOA, DOA, HHO. results demonstrated consistently outperformed other achieving lowest Mean Square Error (MSE) highest classification across all datasets. Notably, reached rate 99.5% Bot-IoT dataset 98.8% CSE-CIC-IDS2018 dataset, underscoring effectiveness classifying diverse threats.

Language: Английский

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Hybrid Four Vector Intelligent Metaheuristic with Differential Evolution for Structural Single-Objective Engineering Optimization

Algorithms, Journal Year: 2024, Volume and Issue: 17(9), P. 417 - 417

Published: Sept. 20, 2024

Complex and nonlinear optimization challenges pose significant difficulties for traditional optimizers, which often struggle to consistently locate the global optimum within intricate problem spaces. To address these challenges, development of hybrid methodologies is essential solving complex, real-world, engineering design problems. This paper introduces FVIMDE, a novel algorithm that synergizes Four Vector Intelligent Metaheuristic (FVIM) with Differential Evolution (DE). The FVIMDE rigorously tested evaluated across two well-known benchmark suites (i.e., CEC2017, CEC2022) an additional set 50 challenging functions. Comprehensive statistical analyses, including mean, standard deviation, Wilcoxon rank-sum test, are conducted assess its performance. Moreover, benchmarked against state-of-the-art revealing superior adaptability robustness. also applied solve five structural challenges. results highlight FVIMDE’s ability outperform existing techniques diverse range problems, confirming potential as powerful tool complex tasks.

Language: Английский

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Optimal Charging Scheduling of Electric vehicles for Smart Grid Operations Employing Demand Side Management strategy with Battery Storage System

2022 4th International Conference on Energy, Power and Environment (ICEPE), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 6

Published: June 20, 2024

Language: Английский

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A Hybrid JADE–Sine Cosine Approach for Advanced Metaheuristic Optimization

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10248 - 10248

Published: Nov. 7, 2024

This paper presents the development and application of JADESCA optimization algorithm for solving complex engineering design problems, including welded beam, pressure vessel, spring, speed reducer problems. JADESCA, a hybrid that combines elements JADE (differential evolution with adaptive parameters) sine cosine (SCA), is evaluated against range benchmark functions from CEC2022 competition as well specific The algorithm’s performance analyzed through convergence curves, search history diagrams, statistical results. In consistently demonstrates superior by achieving optimal or near-optimal solutions high precision consistency. particular, outperforms 25 state-of-the-art optimizers over functions, further proving its robustness adaptability. Statistical comparisons Wilcoxon rank-sum tests reinforce superiority in competitive results across various test cases, solidifying effectiveness handling complex, constrained problems applications.

Language: Английский

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