Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning

Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng

doi:10.1080/0305215x.2015.1005084

Cited by 117 publications

(45 citation statements)

References 14 publications

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“…The objective of hybridizing two meta-heuristic algorithms is to combine the advantages of each algorithm to form an improved one. Such hybridized algorithms used in robot path planning include genetic algorithm and particle swarm optimization (GA-PSO) [28], Multi-Objective Bare Bones Particle Swarm Optimization with Differential Evolution (MOBBPSO) [29], cuckoo search (CS) and bat algorithm (BA) [30] One of the drawbacks in the studies mentioned above is that the mobile robot was treated as a simple particle. While some of these algorithms were oriented toward finding the shortest path avoiding static obstacles.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

151

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The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed PSO-MFB algorithm is highly competitive in terms of path optimality.

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Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

151

View full text Add to dashboard Cite

show abstract

“…○ 2 The value of corresponding objective function is calculated according to the parameters in each particle to select the optimal particle as the temporary global optimal solution. The calculation formula of the objective function [12] is:…”

Section: Motion Planning Optimization Based On the Improved Pso Algormentioning

confidence: 99%

Motion planning optimization of trajectory path of space manipulators

Dong

2019

Int. J. Metrol. Qual. Eng.

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With the development of the aerospace industry, the work carried out inside and outside the weightless space station is becoming more and more complicated. In order to ensure the safety of astronauts, space manipulators are used for operation, but it will disturb the space station that is a base during work. In order to solve the above problems, in this paper, the planning method of the motion trajectory of manipulators, the motion model of manipulators and the particle swarm optimization (PSO) algorithm used for optimizing the trajectory are briefly introduced, the multi-population co-evolution method is used to improve the PSO algorithm, and the above two algorithms are used to optimize the motion trajectory of the floating pedestal space manipulator with three free degrees of rotation in the same plane by the matrix laboratory (MATLAB) software. It is compared with genetic algorithm. The results show that the improved PSO algorithm can converge to a better global optimal fitness with fewer iterations compared with the traditional PSO algorithm and genetic algorithm. The obtained motion trajectory optimized by the improved PSO algorithm has less disturbances to the pedestal posture, and less time is required to achieve the target motion; moreover the changes of mechanical arm joint are more stable during the motion.

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“…A multi-objective fitness function was designed to reduce the energy consumption rate, makespan and load imbalance rate at a same time [40,41,42]. A double global optimum technique was proposed with the help of GA and PSO approaches to remove the premature convergence and convergence speed issue [43]. This technique can efficiently schedule the jobs between cloud data centers in an efficient manner [44].…”

Section: IImentioning

confidence: 99%

An Efficient Load Balancing Technique for Mutation based Particle Swarm Optimization

Sethi¹

2019

IJRASET

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Load balancing techniques play significant role in cloud computing environment because it directly affects the performance of cloud data centers. An efficient load balancing technique not only provides high availability of resources to cloud users, but also enhances the performance of cloud data centers. Load balancing techniques are a typical NP-hard problem. Currently, many researchers have solved load balancing problem by considering well-known metaheuristic techniques. However, these techniques suffer from one of these issues: premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima. To handle the issues associated with existing metaheuristic techniques, in this paper, a mutation based particle swarm optimization based load balancing technique is proposed. The proposed technique has an ability to overcome several issues associated with existing techniques such as premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima issues. Also, multiobjective fitness function is designed as a minimization problem. Multi-objective fitness function considers energy consumption, makespan and load imbalance rate parameters. The proposed technique outperforms existing load balancing techniques in terms of makespan, speedup, communication overheads, efficiency, utilization, mean gain time, load imbalance rate and energy consumption. Index terms: Load balancing, Particle swarm optimization, Cloud computing, Energy efficiency. I. INTRODUCTION Cloud computing is growing phase having a world view of substantial scale distributed computing in internet era [1]. It is a combination of grid and 'X' as a resources in wireless environment. Here, 'X' may be infrastructure, platform, software, data, hardware etc. [2]. The primary objective of scheduling is the assignment of jobs to available set of servers so that execution time can be minimized. Another feature of scheduling is decision-making process and is mostly used in service and manufacturing organizations. Scheduling takes place by taking help of load balancing algorithms [3]. Job is a term related with scheduling used at application level and is a script or program to execute a specific set of jobs. Load balancing algorithm is a technique to which jobs are optimistically assigned to data center resources. There is no completely perfect scheduling mechanism available due to different scheduling objectives. Scheduling algorithms can be executed or implemented under suitable conditions according to assigned applications by a good scheduler. Scheduling algorithm is a mechanism that solves a problem in seconds, minutes or even hours. Time used for execution of particular algorithm measures the efficiency of that algorithm and so time complexity can be measured from the efficiency. Time complexity plays a significant role in time execution of an algorithm. There are some time complexity algorithms used for job execution. The problem is feasible, traceable, fast and efficient in case ...

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Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning

Cited by 117 publications

References 14 publications

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Motion planning optimization of trajectory path of space manipulators

An Efficient Load Balancing Technique for Mutation based Particle Swarm Optimization

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