Particle Swarm Optimizer With Passive Congregation Research Articles

Optimal design of friction tuned mass damper for seismic control of an integral bridge

Vibration control systems such as tuned mass damper (TMD) have drawn considerable attention due to their ability to reduce the seismic response of structures and the consequent damage. The present paper investigates the performance of a new type of TMD, called the friction tuned mass damper (FTMD), in decreasing the seismic response of a four-span integral bridge. The performance of the FTMD in then compared with the TMD under various conditions. The parameters of both dampers are computed via an optimization method. The bridge with damper is then subjected to several strong earthquakes in the transverse direction in different intensity levels, ranging from 0.2 g to 0.8 g. The Particle swarm optimization with passive congregation (PSOPC) is employed and carried out in MATLAB, while the structural modeling and nonlinear dynamic analysis of the bridge is programmed in OpenSees. Numerical results indicate that the FTMD performs better than the TMD in reducing the displacement of the deck. At low intensity of 0.2 g, the reduction in the displacement response of the deck by the TMD and the FTMD are about 28% and 40%, respectively. These values reach 8% and 15% at intensity of 0.8 g. Therefore higher earthquake intensity significantly undermines the performance of both dampers. However the superior performance of the FTMD compared to the TMD is observed.

Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel frames

We propose a new algorithm, named Asymmetric Genetic Algorithm (AGA), for solving optimization problems of steel frames. The AGA consists of a developed penalty function, which helps to find the best generation of the population. The objective function is to minimize the weight of the whole steel structure under the constraint of ultimate loads defined for structural steel buildings by the American Institute of Steel Construction (AISC). Design variables are the cross-sectional areas of elements (beams and columns) that are selected from the sets of side-flange shape steel sections provided by the AISC. The finite element method (FEM) is utilized for analyzing the behavior of steel frames. A 15-storey three-bay steel planar frame is optimized by AGA in this study, which was previously optimized by algorithms such as Particle Swarm Optimization (PSO), Particle Swarm Optimizer with Passive Congregation (PSOPC), Particle Swarm Ant Colony Optimization (HPSACO), Imperialist Competitive Algorithm (ICA), and Charged System Search (CSS). The results of AGA such as total weight of the structure and number of analyses are compared with the results of these algorithms. AGA performs better in comparison to these algorithms with respect to total weight and number of analyses. In addition, five numerical examples are optimized by AGA, Genetic Algorithm (GA), and optimization modules of SAP2000, and the results of them are compared. The results show that AGA can decrease the time of analyses, the number of analyses, and the total weight of the structure. AGA decreases the total weight of regular and irregular steel frame about 11.1% and 26.4% in comparing with the optimized results of SAP2000, respectively.

Optimization of the Distance Between Swarms Using Soft Computing

Particle swarm optimization (PSO) is a dynamic nature-influenced optimization technique. PSO optimization technique can resolve the best solution in minimum iterations and operates more effectively and efficiently. But, the other optimization techniques like particle swarm optimization with passive congregation (PSOPC) technique and dissipative particle swarm optimization (DPSO) technique gives better solution in fewer iterations as compared to PSO. In this paper, the distance between swarms is optimized and compared to all the optimization techniques. Simulation results demonstrate that the PSOPC optimization technique delivers better results than the PSO and DPSO optimization techniques.

GEPSO: A new generalized particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic that has been utilized as a powerful optimization tool in a wide range of applications since its inception in 1995. Due to the flexibility of its parameters and concepts, PSO has appeared in many variants, probably more than any other meta-heuristic algorithm. This paper introduces the Generalized Particle Swarm Optimization (GEPSO) algorithm as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation. These terms aim to deepen the interrelations of particles and their knowledge sharing, increase variety in the swarm, and provide a better search in unexplored areas of the search space. Moreover, a novel procedure is utilized for dynamic updating of the particles’ inertia weights, which controls the convergence of the swarm towards a solution. Also, since parameters of heuristic and meta-heuristic algorithms have a significant influence on their performance, a comprehensive guideline for parameter tuning of the GEPSO is developed. The computational results of solving numerous well-known benchmark functions by the GEPSO, original PSO, Repulsive PSO (REPSO), PSO with Passive Congregation (PSOPC), Negative PSO (NPSO), Deterministic PSO (DPSO), and Line Search-Based Derivative-Free PSO (LS-DF-PSO) approaches showed that the GEPSO outperformed the compared methods in terms of mean and standard deviation of fitness function values and runtimes.

A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

The present paper aims to explore damage assessment methodology based on the changes in dynamic parameters properties of vibration of a structural system. The finite-element model is used to apply at an element level. Reduction of the element stiffness is considered for structural damage. A procedure for locating and quantifying damaged areas of the structure based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for continuous variable optimization. For verifying the method a number of damage scenarios for simulated structures have been considered. For the purpose of damage location and severity assessment the approach is applied in three examples by using complete and incomplete modal data. The effect of noise on the accuracy of the results is investigated in some cases. A great unbraced frame with a lot of damaged element is considered to prove the ability of proposed method. More over BB- BC optimization method in damage detection is compared with particle swarm optimizer with passive congregation (PSOPC) algorithm. This work shows that BB-BC optimization method is a feasible methodology to detect damage location and severity while introducing numerous advantages compared to referred method.

A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

The present paper aims to explore damage assessment methodology based on the changes in dynamic parameters properties of vibration of a structural system. The finite-element model is used to apply at an element level. Reduction of the element stiffness is considered for structural damage. A procedure for locating and quantifying damaged areas of the structure based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for continuous variable optimization. For verifying the method a number of damage scenarios for simulated structures have been considered. For the purpose of damage location and severity assessment the approach is applied in three examples by using complete and incomplete modal data. The effect of noise on the accuracy of the results is investigated in some cases. A great unbraced frame with a lot of damaged element is considered to prove the ability of proposed method. More over BB-BC optimization method in damage detection is compared with particle swarm optimizer with passive congregation (PSOPC) algorithm. This work shows that BB-BC optimization method is a feasible methodology to detect damage location and severity while introducing numerous advantages compared to referred method.

An efficient particle swarm optimization technique with chaotic sequence for optimal tuning and placement of PSS in power systems

This paper presents a novel and efficient method for the optimal tuning and placement of power system stabilizer (PSS). The proposed modified particle swarm optimization (MPSO) integrates the particle swarm optimization with passive congregation (PSOPC) and the chaotic sequence. Passive congregation helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search. In addition, the chaotic sequence concept is introduced to improve the global searching capability and prevent the premature convergence due to local minima. The proposed optimization procedure handles the problem-specific constraints, in this case it is related to low frequency oscillations in power systems and PSS, using a penalty function. Based on this, tuning and placement of PSS over a wide range of system configurations is formulated as a multi-objective function where the main objective is the aggregation of the three objectives related to the damping ratio and damping factor, and the number of PSS. The robustness of the proposed PSS tuning technique is verified on a multi-machine power system under different operating conditions. The performance of the proposed MPSO is also compared to the PSOPS and PSO, genetic algorithm through eigenvalue analysis, nonlinear time-domain simulation and statistical tests. Finally, the proposed method is applied to find out the best candidate machines to be equipped with PSSs. The obtained results showed that the new method can find the best placements and the optimum PSSs parameters simultaneously with an excellent global damping performance.

The Group Search Optimizer and its Application to Truss Structure Design

This paper introduces a novel optimization algorithm, group search optimizer (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO is used to investigate the truss structures with continuous variables and was tested by three truss structure optimization problems. The optimization results were compared with those of the particle swarm optimizer (PSO), the particle swarm optimizer with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO). The calculation results show that the GSO has preferable convergence rate and accuracy. Results from the three tested cases illustrate the competitive ability of the GSO to find the optimal results. It is desired for GSO to be used for structural optimal design problems.

A particle swarm ant colony optimization for truss structures with discrete variables

In this paper, a particle swarm optimizer with passive congregation (PSOPC), ant colony optimization (ACO) and harmony search scheme (HS) are combined to reach to an efficient algorithm, called discrete heuristic particle swarm ant colony optimization (DHPSACO). This method is then employed to optimize truss structures with discrete variables. The DHPSACO applies a PSOPC for global optimization and the ant colony approach for local search, similar to its continuous version. The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints. Some design examples are tested using the new method and their results are compared to those of PSO, PSOPC and HPSO algorithms to demonstrate the effectiveness of the present method.

A heuristic particle swarm optimization method for truss structures with discrete variables

A heuristic particle swarm optimizer (HPSO) algorithm for truss structures with discrete variables is presented based on the standard particle swarm optimizer (PSO) and the harmony search (HS) scheme. The HPSO is tested on several truss structures with discrete variables and is compared with the PSO and the particle swarm optimizer with passive congregation (PSOPC), respectively. The results show that the HPSO is able to accelerate the convergence rate effectively and has the fastest convergence rate among these three algorithms. The research shows the proposed HPSO can be effectively used to solve optimization problems for steel structures with discrete variables.

Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures

A heuristic particle swarm ant colony optimization (HPSACO) is presented for optimum design of trusses. The algorithm is based on the particle swarm optimizer with passive congregation (PSOPC), ant colony optimization and harmony search scheme. HPSACO applies PSOPC for global optimization and the ant colony approach is used to update positions of particles to attain the feasible solution space. HPSACO handles the problem-specific constraints using a fly-back mechanism, and harmony search scheme deals with variable constraints. Results demonstrate the efficiency and robustness of HPSACO, which performs better than the other PSO-based algorithms having higher converges rate than PSO and PSOPC.

Optimal Harmonic Estimation Using A Particle Swarm Optimizer

This paper presents a new algorithm for harmonic estimation. It utilizes the particle swarm optimizer with passive congregation (PSOPC) to estimate the phases of the harmonics, alongside a least-square (LS) method that is used to estimate the amplitudes. The PSOPC and LS method are executed alternately to minimize the error between the original signal and the signal reconstructed from the estimated parameters during the estimation process. Simulation results are presented to demonstrate that the estimation accuracy is greatly improved in comparison with that of the conventional discrete Fourier transform and genetic algorithms. The proposed algorithm is also used to estimate interhar- monics and the harmonics with frequency deviation. The results show that this new method, working in a corporative manner between PSOPC and LS, is capable of estimating power system integral harmonics and interharmonics, even in the case of the deviation of fundamental frequency.

A heuristic particle swarm optimizer for optimization of pin connected structures

This paper presents a heuristic particle swarm optimizer (HPSO) for optimum design of pin connected structures. The algorithm is based on the particle swarm optimizer with passive congregation (PSOPC) and a harmony search (HS) scheme. The HPSO algorithm handles the problem-specific constraints using a ‘fly-back mechanism’ method, and the harmony search scheme deals with the variable constraints. The method is verified and compared with the PSO and PSOPC algorithms used for the designs of five planar and spatial truss structures. The results show that the HPSO algorithm can effectively accelerate the convergence rate and can more quickly reach the optimum design than the two other algorithms.

A particle swarm optimizer with passive congregation

This paper presents a particle swarm optimizer (PSO) with passive congregation to improve the performance of standard PSO (SPSO). Passive congregation is an important biological force preserving swarm integrity. By introducing passive congregation to PSO, information can be transferred among individuals of the swarm. A particle swarm optimizer with passive congregation (PSOPC) is tested with a set of 10 benchmark functions with 30 dimensions and compared to a global version of SPSO (GSPSO), a local version of SPSO (LSPSO), and PSO with a constriction factor (CPSO), respectively. Experimental results indicate that the PSO with passive congregation improves the search performance on the benchmark functions significantly.