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The purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems.

An enhanced particle swarm optimizer (PSO) is used to solve the path planning problem, which concerns the two-dimensional motion of multirotor unmanned aerial vehicles (UAVs) in a three-dimensional environment. Enhancements include an improved initial swarm generation and prediction strategy for succeeding generations. Initial swarm improvements include the clustering process managed by fuzzy c-means clustering method, ordering procedure handled by ant colony optimizer and design vector change. Local solutions form the foundation of a prediction strategy.

Numerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively.

Simulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems.

The proposed method combines intelligent methods in the early phase of PSO, handles obstacle avoidance problems with a unique approach and accelerates the process by adding a prediction strategy.

It is aimed to provide an efficient algorithm for path planning in guidance of autonomous unmanned aerial vehicle (UAV) through 3D terrain environments.

As a stochastic search method, vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for path planning.

Using VGA, an efficient path planning algorithm for autonomous UAV was obtained under low population rate and short generation cycle conditions.

VGA decreased the required time for optimal path solution beside its simplicity. Low population rate and short generation cycle are the main benefits of VGA.

The purpose of this paper is to develop a new genetic optimization strategy which provides computationally more efficient and accurate solutions, and to provide practically applicable optimization method in radar cross‐section (RCS) minimization problems.

The problem of RCS minimization for three‐dimensional air vehicle is considered. New computationally efficient optimization tool; neural networks (NNs) coupled multi‐frequency vibrational genetic algorithm (NN‐coupled VGA^m) is based on genetic algorithm (GA) search strategy together with NNs. The results include RCS minimization problem of an air vehicle under structural and aero dynamical‐related geometry constraints.

For the demonstration problem considered, remarkable reduction in the computational time has been accomplished.

The results reported in this paper suggest an efficient GA optimization methodology for engineering problems.

Owing to reduction in computational time, the new method provides a shorter design cycle for engineering problems.

The purpose of this study is to solve NP-Hard drone routing problem for the last-mile distribution. This is suitable for the multi-drones parcel delivery for the various items from a warehouse to many locations.

This study conducts as a mission assignment of the single location per flight with the constraint satisfactions such as various payloads in weight, drone speeds, flight times and coverage distances. A genetic algorithm is modified as the concurrent heuristics approach (GCH), which has the knapsack problem dealing initialization, gene elitism (crossover) and gene replacement (mutation). Those proposed operators can reduce the execution time consuming and enhance the routing assignment of multiple drones. The evaluation value of the routing assignment can be calculated from the chromosome/individual representation by applying the proposed concurrent fitness.

This study optimizes the total traveling time to accomplish the distribution. GCH is flexible and can provide a result according to the first-come-first-served, demanded weight or distance priority.

GCH is an alternative option, which differs from conventional vehicle routing researches. Such researches (traveling time optimization) attempt to minimize the total traveling time, distance or the number of vehicles by assuming all vehicles have the same traveling speed; therefore, a specific vehicle assignment to a location is neglected. Moreover, the main drawback is those concepts can lead the repeated selection of best quality vehicles concerning the speed without considering the vehicle fleet size and coverage distance while this study defines the various speeds for the vehicles. Unlike those, the concurrent concept ensures a faster delivery accomplishment by sharing the work load with all participant vehicles concerning to their different capabilities. If the concurrent assignment is applied to the drone delivery effectively, the entire delivery can be accomplished relatively faster than the traveling time optimization.

The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems.

The new algorithm emphasizes the use of a direct and an indirect design prediction based on local surrogate models in particle swarm optimization (PSO) algorithm. Local response surface approximations are constructed by using radial basis neural networks. The principal role of surrogate models is to answer the question of which individuals should be placed into the next swarm. Therefore, the main purpose of surrogate models is to predict new design points instead of estimating the objective function values. To demonstrate its merits, the new approach and six comparative algorithms were applied to two different test cases including surface fitting of a geographical terrain and an inverse design of a wing, the averaged best-individual fitness values of the algorithms were recorded for a fair comparison.

The new algorithm provides more than 60 per cent reduction in the required generations as compared with comparative algorithms.

The comparative study was carried out only for two different test cases. It is possible to extend test cases for different problems.

The proposed algorithm can be applied to different inverse design problems.

The study presents extra ordinary application of double surrogate modeling usage in PSO for inverse design problems.