Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

Convergence Analysis of Particle Swarm Optimization via Illustration Styles

2021 ◽  
pp. 29-37
Author(s):  
Wei-Der Chang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Analysis ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Population Based Algorithm

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested

A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures

2018 ◽  
Vol 10 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Zhe Xiong ◽  
Xiao-Hui Li ◽  
Jing-Chang Liang ◽  
Li-Juan Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Pareto Optimality ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Mathematical Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Structural Problems

In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.

Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 18-29
Author(s):  
Rongrong Li ◽  
Linrun Qiu ◽  
Dongbo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Bottom Layer ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Elite Group ◽  
Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

ESTIMATING PARAMETERS OF MUSKINGUM MODEL USING AN ADAPTIVE HYBRID PSO ALGORITHM

2014 ◽  
Vol 28 (01) ◽  
pp. 1459003 ◽  
Author(s):  
AIJIA OUYANG ◽  
ZHUO TANG ◽  
KENLI LI ◽  
AHMED SALLAM ◽  
EDWIN SHA
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Dynamic Models ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Method ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Hybrid Particle Swarm Optimization ◽  
Nonlinear Programming Method

In order to accelerate the convergence and improve the calculation accuracy for parameter optimization of the Muskingum model, we propose a novel, adaptive hybrid particle swarm optimization (AHPSO) algorithm. With the decreasing of inertial weight factor proposed, this method can gradually converge to a global optimal with elite individuals obtained by hybrid PSO. In the paper, we analyzed the feasibility and the advantages of the AHPSO algorithm. Then, we verified its efficiency and superiority by application of the Muskingum model. We intensively evaluated the error fitting degree based on the comparison with four known formulas: the test method (TM), the least residual square method (LRSM), the nonlinear programming method (NPM), and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The results show that the AHPSO has a higher precision. In addition, we compared the AHPSO algorithm with the binary-encoded genetic algorithm (BGA), the Gray genetic algorithm (GGA), the Gray-encoded accelerating genetic algorithm (GAGA) and the particle swarm optimization (PSO), and results show that AHPSO has faster convergent speed. Moreover, AHPSO has a competitive advantage compared with the above eight methods in terms of robustness. With the efficiency of this approach it can be extended to estimate parameters of other dynamic models.

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

scholarly journals Using particle swarm optimization to solve test functions problems

2021 ◽  
Vol 10 (6) ◽  
pp. 3422-3431
Author(s):  
Issa Ahmed Abed ◽  
May Mohammed Ali ◽  
Afrah Abood Abdul Kadhim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Standard Deviation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Speed Of Convergence ◽  
Test Functions ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Engineering Problems

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

Sign in / Sign up

Export Citation Format

Share Document