Research on Hierarchical Cooperative Algorithm Based on Genetic Algorithm and Particle Swarm Optimization

Qiu, Linrun

doi:10.1007/978-981-13-1651-7_2

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The GA has a good global search capability in an area coverage but has poor stability due to large search space complexity, requiring high computation time [196]. Hence, Sadek et al [197] introduced multi-objective GA combining with DP for online CPP, improving the speed of convergence toward the optimal value when a deterministic crossover process replaces the randomized crossover process in GA [198].…”

Section: ) Genetic Evolutionmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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Section: ) Genetic Evolutionmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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“…Besides, some researchers have focused on the research of hybrid PSO algorithms. Jin and Lu [25] propose a multi-subgroup hierarchical hybrid algorithm based on genetic algorithm and PSO (GA-PSO), in which the bottom layer is composed of a series of GA subgroups, which contribute to the global search ability of the algorithm. The upper layer comprises the best individuals in the PSO of each subset, which is helpful for accurate local search of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

An adaptive particle swarm optimization with information interaction mechanism

Liu,

Wei,

Zhang

2024

Mach. Learn.: Sci. Technol.

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The Particle Swarm Optimization (PSO) algorithm is easy to implement owing to its simple framework, and has been successfully applied to many optimization problems. However, the standard PSO easily falls into the local optimum and has weak search ability. To enhance the optimization ability of the algorithm, this paper proposes an adaptive particle swarm optimization with information interaction mechanism (APSOIIM). First, a chaotic sequence strategy was used to produce uniformly distributed particles and enhance their convergence speed at the initialization stage of the algorithm. Then, an interaction information mechanism is introduced to enhance the diversity of the population with the progress of the search, which can effectively interact with the best information of neighboring particles to maintain the balance between exploration and exploitation. Besides, the convergence was proven to verify the robustness and efficiency of the proposed APSOIIM algorithm. Finally, the proposed APSOIIM was applied to solve the CEC2014 benchmark functions and CEC2017 benchmark functions as well as famous engineering optimization problems. The experimental results show that the proposed APSOIIM has significant advantages over the compared algorithms.

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“…Genetic algorithm (GA) is a kind of meta-heuristic random algorithm based on population, inspired by the natural law of biological genetics and the survival and reproduction of the fittest, so as to solve the search problem [14]. Genetic algorithm based CPP algorithms have good global search ability, but they require high computation time due to the large search space and poor stability [15]. Multi-objective genetic algorithm with dynamic programming is proposed to improve the speed of convergence to optimal value [16].…”

Section: Introductionmentioning

confidence: 99%

Predator-Prey Reward Based Q-Learning Coverage Path Planning for Mobile Robot

2023

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Coverage Path Planning (CPP in short) is a basic problem for mobile robot when facing a variety of applications. Q-Learning based coverage path planning algorithms are beginning to be explored recently. To overcome the problem of traditional Q-Learning of easily falling into local optimum, in this paper, the new-type reward functions originating from Predator-Prey model are introduced into traditional Q-Learning based CPP solution, which introduces a comprehensive reward function that incorporates three rewards including Predation Avoidance Reward Function, Smoothness Reward Function and Boundary Reward Function. In addition, the influence of weighting parameters on the total reward function is discussed. Extensive simulation results and practical experiments verify that the proposed Predator-Prey reward based Q-Learning Coverage Path Planning (PP-Q-Learning based CPP in short) has better performance than traditional BCD and Q-Learning based CPP in terms of repetition ratio and turns number.

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Research on Hierarchical Cooperative Algorithm Based on Genetic Algorithm and Particle Swarm Optimization

Cited by 5 publications

References 4 publications

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

An adaptive particle swarm optimization with information interaction mechanism

Predator-Prey Reward Based Q-Learning Coverage Path Planning for Mobile Robot

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