A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle

Aslan, Selçuk; Erkin, Tevfik

doi:10.1016/j.aei.2022.101829

Cited by 22 publications

(1 citation statement)

References 30 publications

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“…The experimental results indicate that this algorithm could perform better in some situations. There are several other metaheuristic-based techniques for tackling the path planning problem of UAV, including the particle swarm optimization algorithm [17,18], a metaheuristic-based imitative learning optimization technique [19], the tabu search-based optimization technique [20], and the memetic algorithm based on two levels [21], bat algorithm improved using fruit fly optimization algorithm [22], dynamic group based cooperation algorithm [7], enhanced ant colony optimization [23], grey wolf optimization algorithm [24], immune plasma algorithm based on multi-population [25], improved symbiotic organisms search [26], GWO improved using both the local memory and the fittest's survival principle [27], genetic algorithm [28], reverse glowworm swarm optimization [29], modified honey badger algorithm [30], immune plasma algorithm [31], multi-objective non-dominated sorting genetic algorithm (NSGA-II) [32], search and rescue optimization algorithm [33], improved chimp optimization algorithm [2], spider monkey optimization [1], and metaheuristic-based hybrid algorithm [34].…”

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An Efficient Evolution-Based Technique for Moving Target Search with Unmanned Aircraft Vehicle: Analysis and Validation

et al. 2023

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Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal’s superiority in terms of the majority of used performance metrics.

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