Hybrid Particle Swarm Optimization Model Research Articles

Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on eight parameters, including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (f_co^'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain (?_co), confined concrete strain (?_cc) and the ultimate compressive strength of confined concrete (f_cc^'). Three metaheuristic models are implemented, including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error, and their predicted results are validated against experimental studies and finite element analysis. The study shows that the hybrid PSO model predicted the strength of CFRP-confined concrete cylinders with a maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale, time-consuming experimental tests that make the process quick and economical.

An Improved Particle Swarm Optimization Algorithm based on Adaptive Genetic Strategy for Global Numerical Optimal

Particle swarm optimization, which has attracted a great deal of attention as a global optimization method in recent years, has the drawback that continuous search based on the excellent dynamic characteristics cannot perform well with higher dimension of particles, especially in real world problems. On the contrary, the strong ability of selection, crossover, and mutation in genetic strategies can realize the double goals of maintaining diversity of population and sustaining the convergence capacity. Thus, in this paper, a hybrid particle swarm optimization model with adaptive genetic strategy based on Euclidean distance is proposed. We consider seven PSO models studied by other researchers, and apply these models to eight well known multi-peaked benchmark functions with dimension of 30, 50, and 100 for comparisons. Numerical simulation results demonstrate the stronger ability of the hybrid model to find the global optimum solutions.

An efficient bi-objective personnel assignment algorithm based on a hybrid particle swarm optimization model

A hybrid particle swarm optimization (HPSO) algorithm which utilizes random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) for solving a bi-objective personnel assignment problem (BOPAP) is presented. The HPSO algorithm which was proposed by Kuo, Horng, Kao, Lin, and Fan (2007) and Kuo et al. (2009b) is used to solve the flow-shop scheduling problem (FSSP). In the research of BOPAP, the main contribution of the work is to improve the f 1_ f 2 heuristic algorithm which was proposed by Huang, Chiu, Yeh, and Chang (2009). The objective of the f 1_ f 2 heuristic algorithm is to get the satisfaction level (SL) value which is satisfied the bi-objective values f 1, and f 2 for the personnel assignment problem. In this paper, PSO is used to search the solution of the input problem in the BOPAP space. Then, with the RK encoding scheme in the virtual space, we can exploit the global search ability of PSO thoroughly. Based on the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of BOPAP based on the proposed HPSO algorithm for the first objective f 1 (i.e., total score), the second objective f 2 (i.e., standard deviation), the coefficient of variance (CV), and the time cost is far better than that of the f 1_ f 2 heuristic algorithm. To the best our knowledge, this presented result of the BOPAP is the best bi-objective algorithm known.

A New Framework for Adaptive Multimodal Biometrics Management

This paper presents a new evolutionary approach for adaptive combination of multiple biometrics to ensure the optimal performance for the desired level of security. The adaptive combination of multiple biometrics is employed to determine the optimal fusion strategy and the corresponding fusion parameters. The score-level fusion rules are adapted to ensure the desired system performance using a hybrid particle swarm optimization model. The rigorous experimental results presented in this paper illustrate that the proposed score-level approach can achieve significantly better and stable performance over the decision-level approach. There has been very little effort in the literature to investigate the performance of an adaptive multimodal fusion algorithm on real biometric data. This paper also presents the performance of the proposed approach from the real biometric samples which further validate the contributions from this paper.

A hybrid particle swarm optimization approach with neural network and set pair analysis for transmission network planning

Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.

An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model

In this paper, a new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP). The objective of FSSP is to find an appropriate sequence of jobs in order to minimize makespan. Makespan means the maximum completion time of a sequence of jobs running on the same machines in flow-shops. By the RK encoding scheme, we can exploit the global search ability of PSO thoroughly. By the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of FSSP based on the proposed HPSO is far better than those based on GA [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851–861.] and NPSO [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851–861.], respectively.

Tribe-PSO: A novel global optimization algorithm and its application in molecular docking

One of the goals of researches on molecular docking is to develop robust algorithms with powerful ability in global optimization because the primary purpose of molecular docking is to find out the most stable conformation of receptor–ligand complexes. Inspired by the principles of Hierarchical Fair Competition, we propose Tribe-PSO, a multi-layered and multi-phased hybrid particle swarm optimization model, in this article. With benchmarks on several widely used multimodal test functions, Tribe-PSO proved to be a powerful global optimization algorithm. Tribe-PSO is then incorporated with the scoring functions in AutoDock 3.05 to perform molecular docking simulations of 100 receptor–ligand complexes from the Protein Data Bank. The simulation results show that the docking procedures using Tribe-PSO method generally find out the stable conformation as well as the native-like complexes more efficiently and accurately than GA–LS combination which is used in AutoDock 3.05 as the optimizer. All the tests demonstrate that Tribe-PSO is a promising search algorithm for molecular docking problems.