###### Abstract  
Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars' attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO. pigeon-inspired optimization; tent map; disturbance mechanism; cooperative target search  
12 July 2021  
Accepted: 4 August 2021  
Published: 10 August 2021  
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**Key words:** Duff,  
ulated annealing algorithm [4]. Reference [5] improves the updating strategy of particles and enhances the robustness of multi-objective particle swarm optimization algorithm in solving permanent magnet optimization problems of electromagnetic devices. In Reference [6], a hybrid multi-objective optimization algorithm combining differential evolution and particle swarm optimization (PSO) is proposed for the contactor (electromagnetic device) optimization problem to accelerate the convergence rate and obtain the limit optimal solution. In Reference [7], a multi-objective optimization method with adaptive weight determination scheme and tunnel method is proposed. By using the concept of hyperplane, the weights of different objective functions are determined adaptively, so that new solutions can be found gradually in the target space. The pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It is widely used in neural network, path planning, and other fields because of its fast convergence speed and easy implementation. PIO algorithm can be divided into map and compass operator and landmark operator. In the map and compass section, the pigeon updates its position and speed according to the global optimal position during each iteration. In the landmark section, the pigeons update their positions with the best position for each iteration. Two operators are employed to guide the pigeons to find the destination. Although PIO algorithm is superior to other intelligent optimization algorithms, such as PSO and DE, it still has the common precocious convergence problem. In the present study, PIO algorithm was optimized. Optimization is mainly divided into two parts: initialization optimization and optimization in the iteration update phase. In the initial part, there are the following studies: [PERSON] et al. added the orthogonal design strategy in the initial stage to improve the diversity of individuals [8]. In Reference [9], Pareto sort is added into the initialization stage to apply to the multi-objective optimization algorithm to improve the efficiency of the algorithm.