Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

2021 ◽  
pp. 095440622199748
Author(s):  
Youyu Liu ◽  
Xuyou Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
B Splines ◽  
Multi Objective ◽  
Searching Strategy ◽  
Better Than

In order to improve the quality of the non-inferior solutions obtained by multi-objective particle swarm optimization (MOPSO), an improved algorithm called external archives self-searching multi-objective particle swarm optimization (EASS-MOPSO) was proposed and applied to a multi-objective trajectory optimization problem for manipulators. The position curves of joints were constructed by using quartic B-splines; the mathematical models of time, energy and jerk optimization objectives for manipulators were established; and the kinematic constraints of joints were transformed into the constraints of the control vertexes of the B-splines. A self-searching strategy of external archives to make non-inferior solutions have the ability to search the surrounding hyperspace was explored, and a diversity maintaining strategy of the external archives was proposed. The results of several test functions by simulation show that the convergence and diversity of the proposed algorithm are better than those of other 4 selected algorithms; the results of the trajectory optimization problem for manipulators by simulation show that the convergence, diversity and time consumption of the proposed algorithm are significantly better than those of non-dominated sorting genetic algorithm.

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

2014 ◽  
Vol 971-973 ◽  
pp. 1242-1246
Author(s):  
Tie Jun Chen ◽  
Yan Ling Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

scholarly journals Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

2021 ◽  
Vol 22 (9) ◽  
pp. 4408
Author(s):  
Cheng-Peng Zhou ◽  
Di Wang ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Protein Structure ◽  
Optimization Problem ◽  
Structure Refinement ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Energy Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Protein Structure Refinement

Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.

scholarly journals Multi-objective reservoir operation of the Ukai reservoir system using improved Jaya algorithm

2021 ◽  
Author(s):  
Vijendra Kumar ◽  
S. M. Yadav
Keyword(s):  
Particle Swarm Optimization ◽  
Reservoir Operation ◽  
Particle Swarm ◽  
Jaya Algorithm ◽  
Hydropower Generation ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Better Than ◽  
Self Adaptive

Abstract This paper introduces an effective and reliable approach based on multi population approach, namely self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results are compared with ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and Invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit was obtained by SAMP-JA and JA as 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation was achieved by SAMP-JA, JA and PSO as 1723.50 . While comparing the average hydropower generation SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, self-adaptive multi-population multi objective Jaya algorithm (SAMP-MOJA) was better than multi objective particle swarm optimization (MOPSO) and multi objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to be outperforming than JA, POS and IWO.

scholarly journals Task-Scheduling Based on Multi-Objective Particle Swarm Optimization in Spatial Crowdsourcing

2019 ◽  
Vol 8 (1) ◽  
pp. 45-57
Author(s):  
Afra A. Alabbadi and Maysoon F. Abulkhair Afra A. Alabbadi and Maysoon F. Abulkhair
Keyword(s):  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Optimization Problem ◽  
Fitness Function ◽  
Particle Swarm ◽  
Physical World ◽  
Spatial Crowdsourcing ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Future Work

As a result of the rapid growth of internet and smartphone technology, a novel platform that attracts individuals and groups known as crowdsourcing emerged. Crowdsourcing is an outsourcing platform that facilitates the accomplishment of costly tasks that consume long periods of time when traditional methods are used. Spatial crowdsourcing (SC) is based on location; it introduces a new framework for the physical world that enables a crowd to complete spatialtemporal tasks. The primary issue in SC is the assignment and scheduling of a set of available tasks to a set of proper workers based on different factors, such as the location of the task, the distance between task location and hired worker location, temporal conditions, and incentive rewards. In the real-world, SC applications need to optimize multi-objectives simultaneously to exploit the utility of SC, and these objectives can be in conflict. However, there are few studies that address this multi-objective optimization problem within a SC environment. Thus, the authors propose a multi-objective task scheduling optimization problem in SC that aims to maximize the number of completed tasks, minimize total travel cost, and ensure worker workload balance. To solve this problem, we developed a method that adapts the multi-objective particle swarm optimization (MOPSO) algorithm based on a proposed novel fitness function. The experiments were conducted with both synthetic and real datasets; the experimental results show that this approach provides acceptable initial results. As future work, we plan to improve the effectiveness of our proposed algorithm by integrating a simple ranking strategy based on task entropy and expected travel costs to enhance MOPSO performance.

scholarly journals New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles

2020 ◽  
Vol 12 (2) ◽  
pp. 168781402090425 ◽  
Author(s):  
Nguyễn Huy Trưởng ◽  
Dinh-Nam Dao
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Mean Square Displacement ◽  
Particle Swarm ◽  
Mean Square ◽  
Stiffness Parameter ◽  
Swarm Optimization ◽  
Multi Objective

In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.

Particle Swarm Optimization for Ship Degaussing Coils Calibration

2012 ◽  
Vol 182-183 ◽  
pp. 1446-1451
Author(s):  
Ming Ming Yang ◽  
Da Ming Liu ◽  
Li Ting Lian
Keyword(s):  
Magnetic Field ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Scale Factors ◽  
Single Objective

In this paper, we deal with the problem of the ship degaussing coils optimal calibration by a linearly decreasing weight particle swarm optimization (LDW-PSO). Taking the ship’s magnetic field and its gradient reduction into account, the problem is treated as a multi-objective optimization problem. First a set of scale factors are calculated by LDW-PSO to scale the two kinds of objective function, then the multi-objective optimization problem is transformed to a single objective optimization problem via a set of proper weights, and the problem is solved by LDW-PSO finally. A typical ship degaussing system is applied to test the method’s validity, and the results are good.

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