Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems

DETDO: An adaptive hybrid dandelion optimizer for engineering optimization

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the intelligent group foraging behaviors exhibited by electric eels in nature. The algorithm mathematically models four key foraging behaviors: interaction, resting, hunting, and migration, to provide both exploration and exploitation during the optimization process. In addition, an energy factor is developed to manage the transition from global search to local search and the balance between exploration and exploitation in the search space. EEFO reveals various foraging patterns based on the foraging characteristics of electric eels. In this study, such dynamic patterns and behaviors are mathematically imitated to design an effective global optimizer. The effectiveness of EEFO is verified through a comparison with 12 other algorithms using the 23 test functions, Congress on Evolutionary Computation 2011 (CEC2011) test suite, and Congress on Evolutionary Computation 2017 (CEC2017) test suite. The experimental results reveal that the EEFO algorithm outperforms the other algorithms for 87% of the 23 test functions and 59% of the CEC2011 test suite, as well as for 66%, 52% and 45% of the CEC2017 test suite with 10, 30, and 50 dimensions, respectively. The applicability of EEFO is comprehensively tested with 10 engineering problems and the application of hydropower station sluice opening control under accident tripping conditions. The results demonstrate the superiority and promising prospects of EEFO when solving a wide range of challenging real-world problems. Overall, the proposed algorithm showcases exceptional performance in terms of exploitation, exploration, the ability to balance exploitation and exploration, and avoiding local optima. EEFO exhibits remarkable competitiveness, particularly in optimization problems that involve unimodal characteristics and numerous constraints and variables. The source code of EEFO is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/153461-electric-eel-foraging-optimization-eefo. 

Electric Eel Foraging Optimization: A new bio-inspired optimizer for engineering applications

A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems

An original math-inspired meta-heuristic algorithm, named quadratic interpolation optimization (QIO), is proposed to address numerical optimization and engineering issues. The main inspiration behind QIO is derived from mathematics, specifically the newly proposed generalized quadratic interpolation (GQI) method. This method overcomes the limitations of the traditional quadratic interpolation method to better find the minimizer of the quadratic function formed by any three points. The QIO utilizes the GQI method as a promising searching mechanism for tackling various types of optimization problems. This searching mechanism delivers exploration and exploitation strategies, in which the minimizer provided by the GQI method assists the QIO algorithm in exploring a promising region in unexplored areas and exploit the optimal solutions in promising regions. To evaluate QIO’s effectiveness, it is comprehensively compared with 12 other commonly used optimizers on 23 benchmark test functions and the CEC-2014 test suite. Ten engineering problems are also tested to assess QIO’s practicality. Eventually, a real-world application of QIO is presented in the operation management of a microgrid with an energy storage system. The results demonstrate that QIO is a promising alternative for addressing practical challenges. The source code of QIO is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/135627-quadratic-interpolation-optimization-qio. 

Quadratic Interpolation Optimization (QIO): A new optimization algorithm based on generalized quadratic interpolation and its applications to real-world engineering problems

An enhanced hybrid arithmetic optimization algorithm for engineering applications

HG-SMA: hierarchical guided slime mould algorithm for smooth path planning

Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation

In modern warfare, unmanned aerial vehicles (UAVs) have become a vital military force. Planning feasible paths for UAVs has become an essential part of forming autonomous attack systems. Aiming at the task requirements of fix-wing UAVs, this paper proposes a ball security corridor planning method by combining the ball Said-Ball curve and a novel super eagle optimization algorithm (SEOA). Firstly, the ball security corridor planning model is established by taking the center curve and radius function of the ball Said-Ball (BSB) curve as the flight path and security boundary. It not only considers the smoothness of the flight path, but also reserves enough security range to avoid cumulative errors. Then, a novel super eagle optimization algorithm is proposed to solve the established model with higher accuracy and efficiency. It simulates the hunting process of virtual super eagles with intelligent thinking. For super eagles, two patterns are designed for determining prey at different stages to avoid premature convergence. An information sharing strategy is also introduced to balance the exploitation and exploration abilities. For prey, they can choose orderly and emergency strategies based on emotional function to escape capture to speed up the convergence of SEOA. According to the results of solving the test function in CEC2017, the SEOA has smaller average rank compared to other flying animal-based algorithms and recent popular algorithms. For the solving successful rate, the SEOA reaches 82.7586% and 89.6552%, respectively. Additionally, the results of Wilcoxon rank-sum test statistically prove that the SEOA is a newly developed intelligent optimization method with significantly higher accuracy. Finally, the SEOA is used to solve the established model by optimizing the positions and radii of control balls of BSB curves. Experimental results in three complex simulation scenes show that the flight corridors obtained by SEOA outperform those generated by other algorithms in overall fitness values. This indicates a more rational optimization in flight altitude, distance, smoothness, and security range. Its faster convergence speed can ensure higher efficiency in planning a security corridor satisfying constraints and requirements. 

Super eagle optimization algorithm based three-dimensional ball security corridor planning method for fixed-wing UAVs

An improved black widow optimization algorithm for surfaces conversion

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