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Moving target search is a challenging dynamic path planning problem. In this scenario, unmanned aerial vehicles endeavor to locate a moving entity based on sensor information, utilizing the optimal path generated by a search algorithm. Based on the Bayesian principle, the task can be transformed into an optimization issue of the fitness function with the maximum probability of capturing the objective. In this study, an improved version of the sand cat swarm optimization algorithm, called the ISCSO search algorithm, is designed to tackle the moving target search issue effectively. Firstly, the presented ISCSO algorithm enhances the planning efficiency of the algorithm by encoding the unmanned aerial vehicle search path information into a set of motion paths through the motion-encoded mechanism. Secondly, the elite pooling strategy and the adaptive T-distribution are constructed to effectively improve the algorithm's ability to escape local optima and enhance its variability. Finally, ISCSO proposes a main architecture that seamlessly merges the search and attack methods of the sand cat swarm optimization algorithm, striking a balance between global and local search capabilities. To evaluate the superiority of the proposed algorithm, nine diverse search scenarios are constructed to verify its performance. The simulation results demonstrate that ISCSO achieves higher detection accuracy and offers more effective search paths for locating dynamic targets in comparison to other well-established metaheuristic algorithms. Code has been available at https://github.com/yb-niu1/ISCSO. • A novel algorithm called ISCSO is proposed to compensate the limitations of existing methods. • The ISCSO provides an innovative solution for UAV moving target search. • The adaptability and accuracy of the ISCSO boosts the efficiency of UAV search. • All results show that ISCSO is an efficient search algorithm. [ABSTRACT FROM AUTHOR]

In complex and volatile unknown flight environments, the limited environmental information obtained by sensors in the face of sudden dynamic and static obstacles makes it extremely challenging for unmanned aerial vehicles (UAVs) to obtain a safe and efficient path to avoid obstacles and reach a designated target point. Therefore, a real-time dynamic path planning method based on an improved interfered fluid dynamical system (IFDS) and artificial neural network (ANN) is proposed to enhance path quality and computational efficiency. Firstly, to address the issue of insufficient sample quality and quantity, IFDS is employed as the fundamental method for path planning to simulate and generate an adequate amount of sample data for the ANN training. Then, an enhanced sand cat swarm optimization algorithm (ESCSO) with an adaptive social neighborhood search mechanism and Lévy flight strategy is proposed to improve the sample quality. Secondly, the information between the UAV and the target points and obstacles is extracted from the sample data as the input for the network, the parameters of the IFDS are used as the feature extraction at the output of the network, and the ESCSO is applied to optimize the weights and biases of the ANN, enabling offline training of the neural network. Finally, the trained neural network is utilized to dynamically output IFDS parameters based on the real-time environmental information obtained from the sensors, enabling the generation of real-time obstacle avoidance paths. Experimental results in a series of complex simulated environments demonstrate that the proposed method outperforms other algorithms in terms of path quality and meets real-time requirements. It provides excellent obstacle avoidance characteristics for the UAV. [ABSTRACT FROM AUTHOR]

• An innovative algorithm called MAEO is proposed. • The MAEO is applied to three-dimensional UCAV path planning. • The MAEO is compared with other state-of-the-art path planners. • Experiments validate the superiority of MAEO. As a challenging high-dimensional multimodal optimization problem, path planning for unmanned combat aerial vehicles (UCAVs) has evolved into a hard optimization problem with multiple objectives and types of constraints in complex operational environments. The traditional approaches lack good search capability in complex multimodal search spaces, resulting in difficulties in providing satisfactory flight paths under multiple constraints. Intelligent optimization algorithms have become the first choice for solving path planning problems because of their excellent global exploration capabilities. Artificial ecosystem optimizer (AEO) is an efficient and intelligent optimization algorithm that has remarkable effectiveness in handling optimization problems. However, it also has the disadvantage of slow convergence. In this work, a modified version of the AEO, named MAEO, is proposed to overcome its shortcomings and apply it to the UCAV path planning problem. In the MAEO algorithm, a new production model that focuses on the current optimal search area is proposed to improve the quality of the producer, thereby effectively guiding consumers to search for the optimal space. Then, an enhanced updating consumption mechanism inspired by animal predation behavior is designed, including two predation operations: one is the introduction of the dynamic elite individual in the original update method to simulate that consumers are also predated by their natural enemies during predation to enhance the convergence speed of the algorithm. The second is a unique spiral predation strategy adopted by the consumers to round up the prey. This mechanism not only effectively avoids the lack of population diversity but also improves the global exploration ability of the algorithm. Furthermore, an adaptive Cauchy mutation strategy is successfully hybridized with the AEO algorithm to boost the ability of the algorithm to escape from the local optimum. Simulation experiments of path planning in a series of complex three-dimensional environments show that the algorithm can plan a path satisfying the constraints stably and efficiently, which proves the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

• An innovative algorithm called NSEAEO is proposed. • The NSEAEO is compared with recent state-of-the-art algorithms. • The NSEAEO is used to solve UCAV path planning on the sea. • All results show that NSEAEO is efficient and effective. Path planning is an essential component of the unmanned combat aerial vehicle (UCAV) system and is the precondition for achieving tasks such as aerial reconnaissance, monitoring, and fire attack. Its objective is to find a satisfactory route with full consideration of the threat area and constraints, which is a multi-constraint global optimization problem. The work proposes an adaptive neighborhood-based search enhanced artificial ecosystem optimizer (NSEAEO) to address the UCAV path planning problem. The new algorithm uses distance-fitness-based information in the consumption phase to construct an adaptive neighborhood for consumers, who choose the better individuals within the neighborhood for predation. The strategy not only facilitates a thorough search of the problem space but also enhances the global exploration capability of the algorithm. In addition, due to the lack of diversity of the population in the decomposition stage of the AEO algorithm, it is easy to fall into the local extremum. A novel updating decomposition mechanism is designed to dynamically choose the decomposition method according to the threshold in each iteration, which effectively averts insufficient diversity and increases the ability to escape from local extrema. Furthermore, to further improve the search capability of the algorithm, a quadratic interpolation (QI) operator is embedded in the AEO algorithm. The core idea is to choose three agents to match a quadratic function close to the goal function and adopt the extremum of the quadratic function to produce new agents. The above approach achieves a good balance between exploitation and exploration. The experimental results on a series of benchmark functions and UCAV path planning in complex environments demonstrate that NSEAEO outperforms other algorithms in terms of solution quality. [ABSTRACT FROM AUTHOR]