Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment

Zhang, Bo; Duan, Haibin

doi:10.1109/tcbb.2015.2443789

Cited by 139 publications

(52 citation statements)

References 33 publications

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“…In the landmark operator, the number of the pigeons is chosen as the numbers of pigeons in PBA, and the fitness is defined as fitness(X i (t))= 1 obj1 min (Xi(t))+obj2 min (Xi(t))+ξ , where obj1 min (X i (t))+obj2 min (X i (t)) is the sum of two objective values. The positions of all pigeons are updated according to (13)- (15), and pigeons are ranked based on the non-dominated-scd-sort algorithm. When all the iterations end, the best pigeons from the PBA represent the final optimization solutions.…”

Section: Multi-objective Pigeon Inspired Optimizationmentioning

confidence: 99%

“…The robustness and effectiveness of the SAPIO algorithm were shown by a number of comparative experiments with other algorithms. Moreover, a novel predator-prey pigeon-inspired optimization (PPPIO) [15] was proposed to solve the uninhabited combat aerial vehicle (UCAV) three-dimension path planning problem in the dynamic environment. The comparative simulation results show that the proposed PPPIO algorithm is more efficient than other algorithms for solving the problem.…”

Section: Introductionmentioning

confidence: 99%

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A multi-objective pigeon inspired optimization algorithm for fuzzy production scheduling problem considering mould maintenance

Chan

Niu

et al. 2019

Sci. China Inf. Sci.

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The fuzzy production scheduling problem considering mould maintenance (FPSP-MM) is studied. The processing time and the maintenance time are represented by triangular fuzzy numbers. When tasks are executed based on the sequence provided by the fuzzy schedule, the real duration of each task needs to be known so the posteriori solution with deterministic processing times can be obtained. Therefore, the concept of the schedule robustness needs to be considered for the fuzzy problem. The robustness is considered as the optimization objective except for the fuzzy makespan in this research. To optimize these two objective functions, a multi-objective pigeon inspired optimization (MOPIO) algorithm is developed. To extend the pigeon inspired optimization (PIO) algorithm from the single-objective case to the multi-objective case, nondominated solutions are used as candidates for the leader pigeon designation and a special crowding distance is used to ensure a good distribution of solutions in both the objective space and the corresponding decision space. Furthermore, an index-based ring topology is used to manage the convergence speed. Numerical experiments on a variety of simulated scenarios show the excellent efficiency and effectiveness of the proposed MOPIO algorithm by comparing it with other algorithms.

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Section: Multi-objective Pigeon Inspired Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-objective pigeon inspired optimization algorithm for fuzzy production scheduling problem considering mould maintenance

Chan

Niu

et al. 2019

Sci. China Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…In order to further improve the convergence rate and avoid the premature convergence problem, new biological behavior characteristics were introduced into PIO. Some predatory-prey pigeon-inspired optimization (PPPIO) algorithms were proposed and applied to UAV path planning in 3D static and dynamic environment [32,33]. The most recent hybrid pigeon-inspired optimization with quantum theory (QPIO) was proposed to solve the premature convergence problem and continuous optimization problems [34].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Xia

Zhang

2018

Algorithms

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Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

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“…In nature, pigeons search their home or destination mainly by three tools: the magnetic field, the sun and landmarks. Inspired by the natural phenomena, the PIO algorithm utilizes two operators to describe the behavior of pigeons [18][19][20][21]. Each pigeon represents a candidate solution of the problem.…”

Section: Introductionmentioning

confidence: 99%

Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design

Deng

Zhu

Duan

2016

Sci. China Technol. Sci.

Self Cite

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In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current (BLDC) motor, which is based on the membrane computing (MC) and pigeon-inspired optimization (PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems. brushless direct current (BLDC) motor, pigeon-inspired optimization (PIO), membrane computing (MC) Citation:Deng Y M, Zhu W R, Duan H B. Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design.

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Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment

Cited by 139 publications

References 33 publications

A multi-objective pigeon inspired optimization algorithm for fuzzy production scheduling problem considering mould maintenance

A multi-objective pigeon inspired optimization algorithm for fuzzy production scheduling problem considering mould maintenance

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design

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