Particle Swarm Methodologies for Engineering Design Optimization

2009 ◽  
Author(s):  
Singiresu S. Rao ◽  
Kiran K. Annamdas
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Multiple Objective ◽  
Optimization Approach ◽  
Objective Functions ◽  
Mixed Design ◽  
Swarm Optimization ◽  
Design Variables ◽  
Mixed Design Variables

Particle swarm methodologies are presented for the solution of constrained mechanical and structural system optimization problems involving single or multiple objective functions with continuous or mixed design variables. The particle swarm optimization presented is a modified particle swarm optimization approach, with better computational efficiency and solution accuracy, is based on the use of dynamic maximum velocity function and bounce method. The constraints of the optimization problem are handled using a dynamic penalty function approach. To handle the discrete design variables, the closest discrete approach is used. Multiple objective functions are handled using a modified cooperative game theory approach. The applicability and computational efficiency of the proposed particle swarm optimization approach are demonstrated through illustrate examples involving single and multiple objectives as well as continuous and mixed design variables. The present methodology is expected to be useful for the solution of a variety of practical engineering design optimization problems.

Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R767-R781 ◽  
Author(s):  
Mattia Aleardi ◽  
Silvio Pierini ◽  
Angelo Sajeva
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Model Space ◽  
Global Search ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Fireworks Algorithm ◽  
Highly Nonlinear ◽  
Whale Optimization

We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular, we are interested in assessing the exploration and exploitation capabilities of each method as the dimension of the model space increases. First, we test the different algorithms on two multiminima and two convex analytic objective functions. Then, we compare them using the residual statics corrections and 1D elastic full-waveform inversion, which are highly nonlinear geophysical optimization problems. Our results demonstrate that FA, FWA, and WOA are characterized by optimal exploration capabilities because they outperform the other approaches in the case of optimization problems with multiminima objective functions. Differently, QPSO and PSO have good exploitation capabilities because they easily solve ill-conditioned optimizations characterized by a nearly flat valley in the objective function. QPSO, PSO, and WCA offer a good compromise between exploitation and exploration.

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

On Global Convergence in Design Optimization Using the Particle Swarm Optimization Technique

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Forrest W. Flocker ◽  
Ramiro H. Bravo
Keyword(s):  
Particle Swarm Optimization ◽  
Global Minimum ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Objective Functions ◽  
Convergence Criteria ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique

The particle swarm optimization (PSO) method is becoming a popular optimizer within the mechanical design community because of its simplicity and ability to handle a wide variety of objective functions that characterize a proposed design. Typical examples arising in mechanical design are nonlinear objective functions with many constraints, which typically arise from the various design specifications. The method is particularly attractive to mechanical design because it can handle discontinuous functions that occur when the designer must choose from a discrete set of standard sizes. However, as in other optimizers, the method is susceptible to converging to a local rather than global minimum. In this paper, convergence criteria for the PSO method are investigated and an algorithm is proposed that gives the user a high degree of confidence in finding the global minimum. The proposed algorithm is tested against five benchmark optimization problems, and the results are used to develop specific guidelines for implementation.

A simple and efficient constrained particle swarm optimization and its application to engineering design problems

2010 ◽  
Vol 224 (2) ◽  
pp. 389-400 ◽  
Author(s):  
T-H Kim ◽  
I Maruta ◽  
T Sugie
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Continuous Type ◽  
Design Variables ◽  
Engineering Design Problems

Engineering optimization problems usually contain various constraints and mixed integer-discrete-continuous type of design variables. This article proposes an efficient particle swarm optimization (PSO) algorithm for such problems. First, the constrained optimization problem is transformed into an unconstrained problem without introducing any problem-dependent or user-defined parameters such as penalty factors or Lagrange multipliers, though such parameters are usually required in general optimization algorithms. Then, the above PSO method is extended to handle integer, discrete, and continuous design variables in a simple manner, yet with a high degree of precision. The proposed PSO scheme is fairly simple and thus it is easy to implement. In order to demonstrate the effectiveness of our method, several mechanical design optimization problems are solved, and the numerical results are compared with those reported in the literature.

scholarly journals Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 146 ◽  
Author(s):  
Ying Sun ◽  
Yuelin Gao ◽  
Xudong Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Feedback Information ◽  
Multi Objective ◽  
Logistic Mapping ◽  
The Status ◽  
Immune Operator

It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved.

Tension-Efficient Cable-Driven Robot Design Using Particle Swarm Optimization

2015 ◽  
Author(s):  
Joshua T. Bryson ◽  
Sunil K. Agrawal
Keyword(s):  
Particle Swarm Optimization ◽  
Stochastic Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Robot Design ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Robot Leg ◽  
Optimization Methodology ◽  
Specific Implementation

Cable-driven robots have advantages which make them attractive solutions for a variety of tasks, however, the unidirectional nature of cable actuators complicates the design and often results in multiply redundant cable architectures which increase cost and robot complexity. This paper presents a stochastic optimization approach to the problem of designing a cable routing for a cable-driven manipulator to provide the desired robot workspace while minimizing the cable tensions required to perform a desired task. Two cable routing design variants are developed for a robot leg through the application of a stochastic optimization methodology called Particle Swarm Optimization. The PSO methodology is summarized, followed by a description of the specific implementation of the methodology to the particular problem of optimizing the cable routing of a robot leg. An objective function is developed to capture all pertinent design criteria in a quantitative evaluation of each particular set of cable parameters. Finally, a description of the PSO execution is presented and the results of the two optimization problems are presented and discussed.

Crosshole traveltime tomography using particle swarm optimization: A near-surface field example

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. R19-R32 ◽  
Author(s):  
Jens Tronicke ◽  
Hendrik Paasche ◽  
Urs Böniger
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Velocity Fields ◽  
P Wave ◽  
Model Parameters ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Near Surface

Particle swarm optimization (PSO) is a relatively new global optimization approach inspired by the social behavior of bird flocking and fish schooling. Although this approach has proven to provide excellent convergence rates in different optimization problems, it has seldom been applied to inverse geophysical problems. Until today, published geophysical applications mainly focus on finding an optimum solution for simple, 1D inverse problems. We have applied PSO-based optimization strategies to reconstruct 2D P-wave velocity fields from crosshole traveltime data sets. Our inversion strategy also includes generating and analyzing a representative ensemble of acceptable models, which allows us to appraise uncertainty and nonuniqueness issues. The potential of our strategy was tested on field data collected at a well-constrained test site in Horstwalde, Germany. At this field site, the shallow subsurface mainly consists of sand- and gravel-dominated glaciofluvial sediments, which, as known from several boreholes and other geophysical experiments, exhibit some well-defined layering at the scale of our crosshole seismic data. Thus, we have implemented a flexible, layer-based model parameterization, which, compared with standard cell-based parameterizations, allows for significantly reducing the number of unknown model parameters and for efficiently implementing a priori model constraints. Comparing the 2D velocity fields resulting from our PSO strategy to independent borehole and direct-push data illustrated the benefits of choosing an efficient global optimization approach. These include a straightforward and understandable appraisal of nonuniqueness issues as well as the possibility of an improved and also more objective interpretation.

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

scholarly journals Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach

2014 ◽  
Vol 4 (3) ◽  
pp. 189-204 ◽  
Author(s):  
Simone A. Ludwig
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Main Idea ◽  
Benchmark Problems ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Wide Range ◽  
Adaptive Pso ◽  
Self Adaptive

Abstract Adaptive Particle Swarm Optimization (PSO) variants have become popular in recent years. The main idea of these adaptive PSO variants is that they adaptively change their search behavior during the optimization process based on information gathered during the run. Adaptive PSO variants have shown to be able to solve a wide range of difficult optimization problems efficiently and effectively. In this paper we propose a Repulsive Self-adaptive Acceleration PSO (RSAPSO) variant that adaptively optimizes the velocity weights of every particle at every iteration. The velocity weights include the acceleration constants as well as the inertia weight that are responsible for the balance between exploration and exploitation. Our proposed RSAPSO variant optimizes the velocity weights that are then used to search for the optimal solution of the problem (e.g., benchmark function). We compare RSAPSO to four known adaptive PSO variants (decreasing weight PSO, time-varying acceleration coefficients PSO, guaranteed convergence PSO, and attractive and repulsive PSO) on twenty benchmark problems. The results show that RSAPSO achives better results compared to the known PSO variants on difficult optimization problems that require large numbers of function evaluations.

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