Search Results (70)

The Tree–Seed Algorithm (TSA) is a symmetry-driven metaheuristic algorithm that shows potential for complex optimization problems, but it suffers from local optimum entrapment and slow convergence. To address these limitations, we propose the ADTSA algorithm. First, ADTSA adopts a symmetry-driven dual-layer framework for seed generation, which promotes effective information exchange between subpopulations and accelerates convergence speed. In later iterations, ADTSA enhances the population’s exploitation ability through a population fusion mechanism, further improving the convergence speed. Moreover, we propose a historical optimal solution archiving and replacement mechanism, along with a t-distribution perturbation mechanism, to enhance the algorithm’s ability to escape local optima. ADTSA also strengthens population diversity and avoids local optima through convex lens symmetric reverse generation based on the optimal solution. With these mechanisms, ADTSA converges more effectively to the global optimum during the evolutionary process. Tests on the IEEE CEC 2014 benchmark functions showed that ADTSA outperformed several top-performing algorithms, such as LSHADE, JADE, LSHADE-RSP, and the latest TSA variants, and it also excelled in comparison with other optimization algorithms, including GWO, PSO, BOA, GA, and RSA, underscoring its robust performance across diverse testing scenarios. The proposed ADTSA’s applicability in solving complex constrained problems was also validated, with the results showing that ADTSA achieved the best solutions for these complex problems. Full article

Multilevel thresholding image segmentation plays a crucial role in various image processing applications. However, achieving optimal segmentation results often poses challenges due to the intricate nature of images. In this study, a novel metaheuristic search algorithm named Weighted Chimp Optimization Algorithm with Fitness–Distance Balance (WChOA-FDB) is developed. The algorithm integrates the concept of Fitness–Distance Balance (FDB) to ensure balanced exploration and exploitation of the solution space, thus enhancing convergence speed and solution quality. Moreover, WChOA-FDB incorporates weighted Chimp Optimization Algorithm techniques to further improve its performance in handling multilevel thresholding challenges. Experimental studies were conducted to test and verify the developed method. The algorithm’s performance was evaluated using 10 benchmark functions (IEEE_CEC_2020) of different types and complexity levels. The search performance of the algorithm was analyzed using the Friedman and Wilcoxon statistical test methods. According to the analysis results, the WChOA-FDB variants consistently outperform the base algorithm across all tested dimensions, with Friedman score improvements ranging from 17.3% (Case-6) to 25.2% (Case-4), indicating that the FDB methodology provides significant optimization enhancement regardless of problem complexity. Additionally, experimental evaluations conducted on color image segmentation tasks demonstrate the effectiveness of the proposed algorithm in achieving accurate and efficient segmentation results. The WChOA-FDB method demonstrates significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) metrics with average enhancements of 0.121348 dB, 0.012688, and 0.003676, respectively, across different threshold levels (m = 2 to 12), objective functions, and termination criteria. Full article

Within computer-aided geometric design (CAGD), Said–Ball curves are primarily adopted in domains such as 3D object skeleton modeling, vascular structure repair, and path planning, owing to their flexible geometric properties. Techniques for curve degree reduction seek to reduce computational and storage demands while striving to maintain the essential geometric attributes of the original curve. This study presents a novel degree reduction model leveraging Euclidean distance and curvature data, markedly improving the preservation of geometric features throughout the reduction process. To enhance performance further, we propose a multi-strategy enhanced coati optimization algorithm (MSECOA). This algorithm utilizes a good point set combined with opposition-based learning to refine the initial population distribution, employs a fitness–distance equilibrium approach alongside a dynamic spiral search strategy to harmonize global exploration with local exploitation, and integrates an adaptive differential evolution mechanism to boost convergence rates and robustness. Experimental results demonstrate that the MSECOA outperforms nine highly cited agorithms in terms of convergence performance, solution accuracy, and stability. The algorithm exhibits superior behavior on the IEEE CEC2017 and CEC2022 benchmark functions and demonstrates strong practical utility across four engineering optimization problems with constraints. When applied to multi-degree reduction approximation of Said–Ball curves, the algorithm’s effectiveness is substantiated through four reduction cases, highlighting its superior precision and computational efficiency, thus providing a highly effective and accurate solution for complex curve degree reduction tasks. Full article

This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration–exploitation balance and local refinement. Validation on the IEEE CEC2017 and CEC2022 benchmark functions demonstrates the EALA’s superior performance, achieving faster convergence and better algorithm performance compared to the standard ALA and 10 other algorithms. When applied to UAV path planning in large- and medium-scale environments with realistic obstacle constraints, the EALA generates Pareto-optimal paths that minimize length, curvature, and computation time while guaranteeing collision avoidance. Benchmark tests and realistic simulations show that the EALA outperforms 10 algorithms. This method is particularly suited for mission-critical applications with strict safety and time constraints. Full article

This paper presents a novel meta-heuristic algorithm inspired by the visual capabilities of the mantis shrimp (Gonodactylus smithii), which can detect linearly and circularly polarized light signals to determine information regarding the polarized light source emitter. Inspired by these unique visual characteristics, the Mantis Shrimp Optimization Algorithm (MShOA) mathematically covers three visual strategies based on the detected signals: random navigation foraging, strike dynamics in prey engagement, and decision-making for defense or retreat from the burrow. These strategies balance exploitation and exploration procedures for local and global search over the solution space. MShOA’s performance was tested with 20 testbench functions and compared against 14 other optimization algorithms. Additionally, it was tested on 10 real-world optimization problems taken from the IEEE CEC2020 competition. Moreover, MShOA was applied to solve three studied cases related to the optimal power flow problem in an IEEE 30-bus system. Wilcoxon and Friedman’s statistical tests were performed to demonstrate that MShOA offered competitive, efficient solutions in benchmark tests and real-world applications. Full article

To better address the issue of multi-threshold image segmentation, this paper proposes a hybrid adaptive crayfish optimization algorithm with differential evolution for color multi-threshold image segmentation (ACOADE). Due to the insufficient convergence ability of the crayfish optimization algorithm in later stages, it is challenging to find a more optimal solution for optimization. ACOADE optimizes the maximum foraging quantity parameter p and introduces an adaptive foraging quantity adjustment strategy to enhance the randomness of the algorithm. Furthermore, the core formula of the differential evolution (DE) algorithm is incorporated to balance ACOADE’s exploration and exploitation capabilities better. To validate the optimization performance of ACOADE, the IEEE CEC2020 test function was selected for experimentation, and eight other algorithms were chosen for comparison. To verify the effectiveness of ACOADE for threshold image segmentation, the Kapur entropy method and Otsu method were used as objective functions for image segmentation and compared with eight other algorithms. Subsequently, the peak signal-to-noise ratio (PSNR), feature similarity index measure (FSIM), structural similarity index measure (SSIM), and Wilcoxon test were employed to evaluate the quality of the segmented images. The results indicated that ACOADE exhibited significant advantages in terms of objective function value, image quality metrics, convergence, and robustness. Full article

This paper proposes a novel hybrid metaheuristic, called JADEGMO, that combines the adaptive parameter control of adaptive differential evolution with optional external archive (JADE) with the search strategies of geometric mean optimizer (GMO). The goal is to enhance both exploration and exploitation stratifies for solving complex optimization tasks. JADEGMO inherits JADE’s adaptive mutation and crossover strategies while leveraging GMO’s swarm-inspired velocity updates guided by elite solutions. The experimental evaluations on IEEE CEC2022 benchmark suites demonstrate that JADEGMO not only achieves superior average performance compared to multiple state-of-the-art methods but also exhibits low variance across repeated runs. Convergence curves, box plots, and rank analyses confirm that JADEGMO consistently finds high-quality solutions while maintaining diversity and avoiding premature convergence. To highlight its applicability, we employ JADEGMO in a real-world multi-cloud security configuration scenario. This problem models the trade-offs among baseline risk, encryption overhead, open ports, privilege levels, and subscription-based security features across three cloud platforms. JADEGMO outperforms other common metaheuristics in locating cost-efficient configurations that minimize risk while balancing overhead and subscription expenses. Full article

Three-dimensional UAV path planning is crucial in practical applications. However, existing metaheuristic algorithms often suffer from slow convergence and susceptibility to becoming trapped in local optima. To address these limitations, this paper proposes a multi-strategy integrated artificial protozoa optimization (IAPO) algorithm for UAV 3D path planning. First, the tent map and refractive opposition-based learning (ROBL) are employed to enhance the diversity and quality of the initial population. Second, in the algorithm’s autotrophic foraging stage, we design a dynamic optimal leadership mechanism, which accelerates the convergence speed while ensuring robust exploration capability. Additionally, during the reproduction phase of the algorithm, we update positions using a Cauchy mutation strategy. Thanks to the heavy-tailed nature of the Cauchy distribution, the algorithm is less likely to become trapped in local optima during exploration, thereby increasing the probability of finding the global optimum. Finally, we incorporate the simulated annealing algorithm into the heterotrophic foraging and reproduction stages, effectively preventing the algorithm from getting trapped in local optima and reducing the impact of inferior solutions on the convergence efficiency. The proposed algorithm is validated through comparative experiments using 12 benchmark functions from the 2022 IEEE Congress on Evolutionary Computation (CEC), outperforming nine common algorithms in terms of convergence speed and optimization accuracy. The experimental results also demonstrate IAPO’s superior performance in generating collision-free and energy-efficient UAV paths across diverse 3D environments. Full article

Path planning is crucial for safely and efficiently navigating unmanned aerial vehicles (UAVs) toward operational goals. Often, this is a complex, multi-constraint, and non-linear optimization problem, and metaheuristic algorithms are frequently used to solve it. Grey Wolf Optimization (GWO) is one of the most popular algorithms for solving such problems. However, standard GWO has several limitations, such as premature convergence, susceptibility to local minima, and unsuitability for dynamic environments due to its lack of adaptive learning. We propose a Q-learning-based GWO algorithm to address these issues in this study. QGWO introduces four key features: a Q-learning-based adaptive convergence factor, a segmented and parameterized position update strategy, a long-jump mechanism for population diversity preservation, and the replacement of non-dominant wolves for improved exploration. In addition, the Bayesian optimization algorithm is used to set parameters in QGWO for better performance. To evaluate the quality and robustness of QGWO, extensive numerical and simulation experiments were conducted on IEEE CEC 2022 benchmark functions, comparing it with standard GWO and some of its recent variants. In path planning simulation, QGWO lowers the path cost by 27.4%, improves the convergence speed by 19.06%, and reduces the area under the curve (AUC) by 23.8% over standard GWO, achieving optimal trajectory. Results show that QGWO is an efficient, reliable algorithm for UAV path planning in dynamic environments. Full article

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

The electrical storm optimization (ESO) algorithm, inspired by the dynamic nature of electrical storms, is a novel population-based metaheuristic that employs three dynamically adjusted parameters: field resistance, field intensity, and field conductivity. Field resistance assesses the spread of solutions within the search space, reflecting strategy diversity. The field intensity balances the exploration of new territories and the exploitation of promising areas. The field conductivity adjusts the adaptability of the search process, enhancing the algorithm’s ability to escape local optima and converge on global solutions. These adjustments enable the ESO to adapt in real-time to various optimization scenarios, steering the search toward potential optima. ESO’s performance was rigorously tested against 60 benchmark problems from the IEEE CEC SOBC 2022 suite and 20 well-known metaheuristics. The results demonstrate the superior performance of ESOs, particularly in tasks requiring a nuanced balance between exploration and exploitation. Its efficacy is further validated through successful applications in four engineering domains, highlighting its precision, stability, flexibility, and efficiency. Additionally, the algorithm’s computational costs were evaluated in terms of the number of function evaluations and computational overhead, reinforcing its status as a standout choice in the metaheuristic field. Full article

The resolution of the unmanned aerial vehicle (UAV) path-planning problem frequently leverages optimization algorithms as a foundational approach. Among these, the recently proposed crayfish optimization algorithm (COA) has garnered significant attention as a promising and noteworthy alternative. Nevertheless, COA’s search efficiency tends to diminish in the later stages of the optimization process, making it prone to premature convergence into local optima. To address this limitation, an improved COA (ICOA) is proposed. To enhance the quality of the initial individuals and ensure greater population diversity, the improved algorithm utilizes chaotic mapping in conjunction with a stochastic inverse learning strategy to generate the initial population. This modification aims to broaden the exploration scope into higher-quality search regions, enhancing the algorithm’s resilience against local optima entrapment and significantly boosting its convergence effectiveness. Additionally, a nonlinear control parameter is incorporated to enhance the algorithm’s adaptivity. Simultaneously, a Cauchy variation strategy is applied to the population’s optimal individuals, strengthening the algorithm’s ability to overcome stagnation. ICOA’s performance is evaluated by employing the IEEE CEC2017 benchmark function for testing purposes. Comparison results reveal that ICOA outperforms other algorithms in terms of optimization efficacy, especially when applied to complex spatial configurations and real-world problem-solving scenarios. The proposed algorithm is ultimately employed in UAV path planning, with its performance tested across a range of terrain obstacle models. The findings confirm that ICOA excels in searching for paths that achieve safe obstacle avoidance and lower trajectory costs. Its search accuracy is notably superior to that of the comparative algorithms, underscoring its robustness and efficiency. ICOA ensures the balanced exploration and exploitation of the search space, which are particularly crucial for optimizing UAV path planning in environments with symmetrical and asymmetrical constraints. Full article

This article proposes an improved algorithm for the parameter identification of permanent magnet synchronous motors (PMSMs). An enhanced raccoon optimization algorithm (EROA) was formed by combining the raccoon optimization algorithm (ROA) with the adaptive exploration radius, raccoon-washing-food-inspired, and escaping-predator strategies. First, using some of the functions in IEEE CEC2015, the EROA solution has a large improvement in convergence speed and solution accuracy compared with other algorithms. Second, the EROA solution is more stable under the same conditions, as demonstrated by MATLAB parameter identification simulation. Finally, EROA is applied to motor parameter identification through motor control experiments. Full article

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs. Full article

The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems. Full article