Zahra Beheshti

CAPSO: Centripetal accelerated particle swarm optimization

Meta-heuristic search algorithms are developed to solve optimization problems. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. Swarm intelligence (SI) algorithms comprise a branch of meta-heuristic algorithms that imitate the behavior of insects, birds, fishes, and other natural phenomena to find solutions for complex optimization problems. In this study, an improved particle swarm optimization (PSO) scheme combined with Newtons laws of motion, the centripetal accelerated particle swarm optimization (CAPSO) scheme, is introduced. CAPSO accelerates the learning and convergence of optimization problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. These algorithms are evaluated using nonlinear benchmark functions, and the results are compared with the gravitational search algorithm (GSA) and PSO in both the real and the binary search spaces. Moreover, the performance of CAPSO in solving the functions is compared with some well-known PSO algorithms in the literature. The experimental results showed that the proposed methods enhance the performance of PSO in terms of convergence speed, solution accuracy and global optimality.

Memetic binary particle swarm optimization for discrete optimization problems

In recent decades, many researchers have been interested in algorithms inspired by the observation of natural phenomena to solve optimization problems. Among them, meta-heuristic algorithms have been extensively applied in continuous (real) and discrete (binary) search spaces. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. In this study, a memetic binary particle swarm optimization (BPSO) scheme is introduced based on hybrid local and global searches in BPSO. The algorithm, binary hybrid topology particle swarm optimization (BHTPSO), is used to solve the optimization problems in the binary search spaces. In addition, a variant of the proposed algorithm, binary hybrid topology particle swarm optimization quadratic interpolation (BHTPSO-QI), is proposed to enhance the global searching capability. These algorithms are tested on two set of problems in the binary search space. Several nonlinear high-dimension functions and benchmarks for the 0-1 multidimensional knapsack problem (MKP) are employed to evaluate their performances. Their results are compared with some well-known modified binary PSO and binary gravitational search algorithm (BGSA). The experimental results showed that the proposed methods improve the performance of BPSO in terms of convergence speed and solution accuracy.

MPSO: Median-oriented Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a bio-inspired optimization algorithm which has been empirically demonstrated to perform well on many optimization problems. However, it has two main weaknesses which have restricted the wider applications of PSO. The algorithm can easily get trapped in the local optima and has slow convergence speed. Therefore, improvement and/or elimination of these disadvantages are the most important objective in PSO research. In this paper, we propose Median-oriented Particle Swarm Optimization (MPSO) to carry out a global search over entire search space with accelerating convergence speed and avoiding local optima. The median position of particles and the worst and median fitness values of the swarm are incorporated in the standard PSO to achieve the mentioned goals. The proposed algorithm is evaluated on 20 unimodal, multimodal, rotated and shifted high-dimensional benchmark functions and the results are compared with some well-known PSO algorithms in the literature. The results show that MPSO substantially enhances the performance of the PSO paradigm in terms of convergence speed and finds global or good near-global optimal in the functions.

Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems

The majority of Combinatorial Optimization Problems (COPs) are defined in the discrete space. Hence, proposing an efficient algorithm to solve the problems has become an attractive subject in recent years. In this paper, a meta-heuristic algorithm based on Binary Particle Swarm Algorithm (BPSO) and the governing Newtonian motion laws, so-called Binary Accelerated Particle Swarm Algorithm (BAPSA) is offered for discrete search spaces. The method is presented in two global and local topologies and evaluated on the 0–1 Multidimensional Knapsack Problem (MKP) as a famous problem in the class of COPs and NP-hard problems. Besides, the results are compared with BPSO for both global and local topologies as well as Genetic Algorithm (GA). We applied three methods of Penalty Function (PF) technique, Check-and-Drop (CD) and Improved Check-and-Repair Operator (ICRO) algorithms to solve the problem of infeasible solutions in the 0–1 MKP. Experimental results show that the proposed methods have better performance than BPSO and GA especially when ICRO algorithm is applied to convert infeasible solutions to feasible ones.

Non-parametric particle swarm optimization for global optimization

Proposing an improved PSO scheme called non-parametric particle swarm optimization (NP-PSO).Combining local and global topologies with two quadratic interpolation operations to increase the search ability in NP-PSO.Removing PSO parameters in the proposed method.Having the best performance of NP-PSO in solving various nonlinear functions compared with some well-known PSO algorithms. In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability.

Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis

In recent decades, artificial neural networks (ANNs) have been extensively applied in different areas such as engineering, medicine, business, education, manufacturing and so on. Nowadays, ANNs are as a hot research in medicine especially in the fields of medical disease diagnosis. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. ANN learning is a complex task and an efficient learning algorithm has a significant role to enhance ANN performance. In this paper, a new meta-heuristic algorithm, centripetal accelerated particle swarm optimization (CAPSO), is applied to evolve the ANN learning and accuracy. The algorithm is based on an improved scheme of particle swarm algorithm and Newton’s laws of motion. The hybrid learning of CAPSO and multi-layer perceptron (MLP) network, CAPSO-MLP, is used to classify the data of nine standard medical datasets of Hepatitis, Heart Disease, Pima Indian Diabetes, Wisconsin Prognostic Breast Cancer, Parkinson’s disease, Echocardiogram, Liver Disorders, Laryngeal 1 and Acute Inflammations. The performance of CAPSO-MLP is compared with those of PSO, gravitational search algorithm and imperialist competitive algorithm on MLP. The efficiency of methods are evaluated based on mean square error, accuracy, sensitivity, specificity, area under the receiver operating characteristics curve and statistical tests of

Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems

t

A Review of Emotional Learning And It's Utilization in Control Engineering

t-test and Wilcoxon’s signed ranks test. The results indicate that CAPSO-MLP provides more effective performance than the others for medical disease diagnosis especially in term of unseen data (testing data) and datasets with high missing data values.