Haibin Duan

Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

Purpose – The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems. Design/methodology/approach – The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted. Findings – The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases. Originality/value – In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is prese...

?Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration

The initial state of an Unmanned Aerial Vehicle (UAV) system and the relative state of the system, the continuous inputs of each flight unit are piecewise linear by a Control Parameterization and Time Discretization (CPTD) method. The approximation piecewise linearization control inputs are used to substitute for the continuous inputs. In this way, the multi-UAV formation reconfiguration problem can be formulated as an optimal control problem with dynamical and algebraic constraints. With strict constraints and mutual interference, the multi-UAV formation reconfiguration in 3-D space is a complicated problem. The recent boom of bio-inspired algorithms has attracted many researchers to the field of applying such intelligent approaches to complicated optimization problems in multi-UAVs. In this paper, a Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) is proposed to solve the multi-UAV formation reconfiguration problem, which is modeled as a parameter optimization problem. This new approach combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which can find the time-optimal solutions simultaneously. The proposed HPSOGA will also be compared with basic PSO algorithm and the series of experimental results will show that our HPSOGA outperforms PSO in solving multi-UAV formation reconfiguration problem under complicated environments.

Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft

This paper describes a novel shape-matching approach to visual target recognition for aircraft at low altitude. An artificial bee colony (ABC) algorithm with edge potential function (EPF) is proposed to accomplish the target recognition task for aircraft. EPF is adopted to provide a type of attractive pattern for a matching contour, which can be exploited by ABC algorithm conveniently. In this way, the best match can be obtained when the sketch image translates, reorients and scales itself to maximize the potential value. In addition, the convergence proof and computational complexity for the ABC algorithm are also given in detail. Series of experimental results demonstrate the feasibility and effectiveness of our proposed approach over the traditional genetic algorithm (GA). The proposed method can also be applied to solve the target recognition problems in mobile robots, industry production lines, and transportations.

Template matching using chaotic imperialist competitive algorithm

Image matching plays an important role in feature tracking, object recognition, stereo matching, digital photogrammetry, remote sensing, and computer vision. Imperialist competitive algorithm (ICA) is inspired by imperialistic competition mechanism. In this paper, we present a novel template matching method based on chaotic ICA. Based on the introduction of the principle of ICA, the correlation function used in this approach is proposed. The chaos can improve the global convergence of ICA, and the phenomena of falling into local best solution can be prevented. The detailed process for chaotic ICA-based template matching is also presented in detail. The three typical comparative results show that our proposed chaotic ICA image matching approach is more efficient and effective than the basic ICA.

A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

Predator–Prey Brain Storm Optimization for DC Brushless Motor

Brain Storm Optimization (BSO) is a newly-developed swarm intelligence optimization algorithm inspired by a human beings behavior of brainstorming. In this paper, a novel predator-prey BSO model, which is named Predator-prey Brain Storm Optimization (PPBSO), is proposed to solve an optimization problem modeled for a DC brushless motor. The Predator-prey concept is adopted to better utilize the global information and improve the swarm diversity during the evolution process. The proposed algorithm is applied to solve the optimization problems in an electromagnetic field. The comparative results demonstrate that both PPBSO and BSO can succeed in optimizing design variables for a DC brushless motor to maximize its efficiency. Simulation results show PPBSO has better ability to jump out of local optima when compared with the original BSO. In addition, it demonstrates satisfactory stability in repeated experiments.

Optimal Satellite Formation Reconfiguration Based on Closed-Loop Brain Storm Optimization

In recent years, satellite formation flying has become an increasingly hot topic for both the astronomy and earth science communities due to its potential merits compared with a single monolithic spacecraft system. This paper proposes a novel approach based on closed-loop brain storm optimization (CLBSO) algorithms to address the optimal formation reconfiguration of multiple satellites using two-impulse control. The optimal satellite formation reconfiguration is formulated as an optimization problem with the constraints of overall fuel cost minimization, final configuration, and collision avoidance. Three versions of CLBSOs are developed by replacing the creating operator in basic brain storm optimization (BSO) with closed-loop strategies, which facilitate search characteristic capture and enhance the optimization performance by taking advantage of feedback information in the search process. Numerical simulations are carried out using particle swarm optimization (PSO), basic BSO, and the three versions of CLBSOs. Comparison results show that all versions of CLBSOs outperform PSO and the original BSO in terms of final results and convergence speed. In addition, CLBSO reduces the computation burden and shortens CPU time to a certain extent in contrast with basic BSO. Furthermore, among the three CLBSO algorithms, the one using the strategy of difference with the best gains the best overall performance, which is inspired by the updating rule in PSO that each particle tends to move towards the individual with the best fitness.

Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm

Abstract Three-dimension path planning of uninhabited combat air vehicle (UCAV) is a complicated optimal problem, which mainly focuses on optimizing the flight route considering the different types of constrains under complicated combating environments. A new hybrid meta-heuristic ant colony optimization (ACO) and differential evolution (DE) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the pheromone trail of the improved ACO model during the process of ant pheromone updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the three-dimensional mesh while avoiding the threats area and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic ACO. The realization procedure for this hybrid meta-heuristic approach is also presented in detail. In order to make the optimized UCAV path more feasible, the к-trajectory is adopted for smoothing the path. Finally, series experimental comparison results demonstrate that this proposed hybrid meta-heuristic method is more effective and feasible in UCAV three-dimension path planning than the basic ACO model.

Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm, an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.

Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments

Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.