scholarly journals A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2286
Author(s):  
Xiaoman Cao ◽  
Hansheng Yan ◽  
Zhengyan Huang ◽  
Si Ai ◽  
Yongjun Xu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Evaluation Method ◽  
Technical Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Advantages And Disadvantages ◽  
Optimal Evaluation

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

Hybrid Immune Clonal Particle Swarm Optimization Multi-Objective Algorithm for Constrained Optimization Problems

2017 ◽  
Vol 31 (01) ◽  
pp. 1759001 ◽  
Author(s):  
Shengyu Pei
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Standard Test ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Local Optimal Solution

How to solve constrained optimization problems constitutes an important part of the research on optimization problems. In this paper, a hybrid immune clonal particle swarm optimization multi-objective algorithm is proposed to solve constrained optimization problems. In the proposed algorithm, the population is first initialized with the theory of good point set. Then, differential evolution is adopted to improve the local optimal solution of each particle, with immune clonal strategy incorporated to improve each particle. As a final step, sub-swarm is used to enhance the position and velocity of individual particle. The new algorithm has been tested on 24 standard test functions and three engineering optimization problems, whose results show that the new algorithm has good performance in both robustness and convergence.

scholarly journals Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)

2020 ◽  
Vol 6 (1) ◽  
pp. 72
Author(s):  
Alwatben Batoul Rashed ◽  
Hazlina Hamdan ◽  
Nurfadhlina Mohd Sharef ◽  
Md Nasir Sulaiman ◽  
Razali Yaakob ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Technique ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Crowding Distance

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.

scholarly journals Adaptive Multi-Objective Particle Swarm Optimization Using Three-Stage Strategy

2021 ◽  
Author(s):  
Weimin Huang ◽  
Wei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Strategy ◽  
Adaptive Optimization ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multi Objective ◽  
Optimal Solution Set ◽  
Optimization Efficiency

Abstract It is one of the crucial problems in solving multi-objective problems (MOPs) that balance the convergence and diversity of the algorithm to obtain an outstanding Pareto optimal solution set. In order to elevate the performance further and improve the optimization efficiency of multi-objective particle swarm optimization (MOPSO), a novel adaptive MOPSO using a three-stage strategy (tssAMOPSO) is proposed in this paper, which can effectively balance the exploration and exploitation of the population and facilitate the convergence and diversity of MOPSO. Firstly, an adaptive flight parameter adjustment, formulated by the convergence contribution of nondominated solutions, can ameliorate the convergence and diversity of the algorithm enormously. Secondly, the population carries out the three-stage strategy of optimization in each iteration, namely adaptive optimization, decomposition, and Gaussian attenuation mutation. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Moreover, the convergence of three-stage optimization strategy is analyzed. Then, memory interval is equipped with particles to record the recent positions, which vastly improves the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of nondominated solutions directly. Finally, comparative experiments are designed by a series of benchmark instances to verify the performance of tssAMOPSO. Experimental results show that the proposed algorithm achieves admirable performance compared with other contrast algorithms.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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