A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning

Das, Pradipta Kumar; Behera, Himansu Sekhar; Panigrahi, B. K.

doi:10.1016/j.swevo.2015.10.011

Cited by 237 publications

(91 citation statements)

References 52 publications

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“…Wang and Song [36] introduced a small constant into hybrid algorithm based on GSA and PSO for updating strategy to obtain four improved hybrid algorithms in nonlinear function optimization problems. Das et al [37] use a new methodology to determine the optimal trajectory of the path for multirobots in a clutter environment using a hybrid approach of improved PSO with the local best value and improved GSA with the velocity updated by introducing a random constant.…”

Section: The Prior Workmentioning

confidence: 99%

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Xia-xia

Bai

2017

Advances in Mathematical Physics

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Gravitational Search Algorithm (GSA) is a widely used metaheuristic algorithm. Although fewer parameters in GSA were adjusted, GSA has a slow convergence rate. In this paper, we change the constant acceleration coefficients to be the exponential function on the basis of combination of GSA and PSO (PSO-GSA) and propose an improved PSO-GSA algorithm (written as I-PSO-GSA) for solving two kinds of classifications: surface water quality and the moving direction of robots. I-PSO-GSA is employed to optimize weights and biases of backpropagation (BP) neural network. The experimental results show that, being compared with combination of PSO and GSA (PSO-GSA), single PSO, and single GSA for optimizing the parameters of BP neural network, I-PSO-GSA outperforms PSO-GSA, PSO, and GSA and has better classification accuracy for these two actual problems.

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Section: The Prior Workmentioning

confidence: 99%

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Xia-xia

Bai

2017

Advances in Mathematical Physics

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“…In the hybrid particle swarm gravitational search algorithm equation, C 3 and C 4 for two acceleration coefficient, used to adjust the speed of particle swarm optimization (pso) algorithm and the acceleration of gravitational search algorithm, Φ 3 is any number of [0, 1], the size of its value to determine the speed of particle swarm and the acceleration of gravitational search algorithm proportion [1].…”

Section: Distributed Control Algorithmmentioning

confidence: 99%

A Heuristic Group Intelligence Control System Based on Hybrid Particle Swarm Gravitational Search Algorithm

Ding¹,

Wang²,

Hu³

et al. 2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-Cluster control system to realize mutual coordination between individual objects must determine the control and the information relationship in terms of logical and physical aspects. Study these problems of the system structure, system structure and control can be combined and ensure smooth information flow and control flow in the system and the framework for the interaction between the individual. Cluster control algorithm ensure cooperation among multiple control individual effectively, to deal with an emergency to be able to react quickly, improve the real-time performance and effectiveness of control system. This paper proposed a new methodology to Group Intelligence Control by using a distributed control algorithm and a Swarm Intelligence algorithm to optimize the path of the individual of the cluster and the real-time control of each object. In the first part of this paper, we introduced the appearance and the development of Group Intelligence Control system. Then, the paper proposed our methodology of the proposed Group Intelligence Control system.

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“…In recent decades, inspired by the organized behavior of natural biological groups, numerous swarm-intelligence optimization algorithms have been proposed to be applied to UAV path planning problem [11,12]. Notable examples include ant colony optimization algorithm (ACO) [13], particle swarm optimization algorithm (PSO) [14], fruit fly optimization algorithm (FOA) [15], and pigeon-inspired optimization algorithm (PIO) [16].…”

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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A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning

Cited by 237 publications

References 52 publications

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

A Heuristic Group Intelligence Control System Based on Hybrid Particle Swarm Gravitational Search Algorithm

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

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