Classical PSO Algorithm Research Articles

A Novel Fuzzy Logic-Based Improved Cuckoo Search Algorithm

Cuckoo Search (CS) algorithm is a nature-inspired optimization algorithm (NIOA) with less control parameters that is stable, versatile, and easy to implement. CS has good global search capabilities, but it is prone to local optima problems. As a result, it may be possible to improve the classic CS algorithm's optimization capability. Centered on fuzzy set theory, this paper introduces an improved CS version. The population of solutions has been divided into two fuzzy sets, and each solution is assigned to one of the sets based on its fitness. The fuzzy collection centroids, global best solution advice, and Lévy distribution dependent mutation are all used to boost the population's solutions. With well-accepted objective functions such as Otsu inter class variance and Kapur's entropy, the experimental analysis has been conducted on the CEC-2014 test suite and image multi-level thresholding domain. The proposed fuzzy cuckoo search (FCS) algorithm is compared to the classical CS, PSO, FA, SMA, and BA algorithm and provides satisfactory results.

Self-Regulated Particle Swarm Multi-Task Optimization.

Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.

Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

A significant problem in the control field is the adjustment of PID controller parameters. Because of its high nonlinearity property, control of the DC motor system is difficult and mathematically repetitive. The particle swarm optimization PSO solution is a great optimization technique and a promising approach to address the problem of optimum PID controller results. In this paper, a modified particle swarm optimization PSO method with four inertia weight functions is suggested to find the global optimum parameters of the PID controller for speed and position control of the DC motor. Benchmark studies of inertia weight functions are described. Two scenarios have been suggested in order to modify PSO including the first scenario called M1-PSO and the second scenario called M2-PSO, as well as classical PSO algorithms. For the first scenario, the modification of the PSO was done based on changing the four inertia weight functions, social and personal acceleration coefficient, while in the second scenario, the four inertia weight functions have been changed but the social and personal acceleration coefficient stayed constant during the algorithm implementation. The comparison between the presented scenarios and traditional PID was carried out and satisfied simulation results have shown that the first scenario has rapid search speeds, and very effective and fast implementation compared to the second scenario and classical PSO and even improved PSO technique. Moreover, the proposed approach has a fast searching speed compared to classical PSO. However, it has been found that the classical PSO algorithm has a premature, inaccurate and local convergence process when solving complex optimization issues. The presented algorithm is proposed to increase the search speed of the original PSO.

Robust decoding strategy of MIMO-STBC using one source Kurtosis based GPSO algorithm

Most research papers have referred that the performance of bit error rate (BER) for a Multiple-Input Multipl-Output Space–Time Block Code (MIMO-STBC) decoder fundamentally relies on the number of pilot symbols. This paper proposes a new strategy for decoding a MIMO-STBC using blind source separation (BSS). First, the STBC decoder is modeled as a noisy linear mixing system, which is decomposed using the technique of real-imaginary (Re-Im). Then Kurtosis based BSS algorithm is applied to the decomposition model. The method of one source extraction is also used to reduce decoding time efficiently. Finally, a global particle swarm optimization method (GPSO) is combined with the one source extraction Kurtosis based BSS to obtain a high speed/low complexity MIMO-STBC decoder. The classical PSO algorithm in this paper is modified by innovating a new updating formula, which is the combination of the swarm behavior and BSS algorithm. Although the new decoder is more complicated as compared with the conventional one, it provides superior BER performance using a fewer number of pilot symbols. Computer simulation for QPSK STBC in a quasi-static flat fading MIMO channel was implemented using MATLAB2018. The new decoder algorithm was tested using only two receivers, worst case. The important point denoted through simulation is the BER performance of the proposed decoder was significantly got better when the length of frames gets longer. The proposed strategy was also used in decoding the $$8\times 8$$ MIMO-STBC system in an extreme noise environment. It was found the BER performance improved nine times as compared with the traditional decoder. Finally, the modified GPSO algorithm made the proposed decoder needs a very small number of iterations, even though the search space dimension is very high.

Analysis of the Application of Artificial Intelligence in Computer Networks Technology

With the development of information technology and the continuous improvement of development level, artificial intelligence technology has also made better development. There are more and more application scenarios of artificial intelligence technology in computer networks, and it also plays a key role. Aiming at the shortcomings of traditional computer network routing methods such as slow iterative convergence and high resource occupancy, this paper proposes an artificial intelligence routing method based on optimized PSO. Based on the principle of the PSO algorithm, a mathematical model for optimal link optimization of a computer network is constructed, and local optimization is performed for the shortcomings of the classic PSO algorithm. The optimal path labelling function is given to the particles, and the optimal network is identified by continuously updating the current position and current speed. Link topology and travel path. Simulation results show that the proposed artificial intelligence method has faster iterative optimization speed, lower network delay, lower hardware resource occupancy under the same number of nodes and network structure, and can improve and improve the foundation of computer networks.

Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model

The classical PSO algorithm can be affected with premature convergence when it comes to more complex optimization problems; the resolution easily can be trapped into local optima. The primary concern is to accelerate the convergence speed and to prevent the local optima solutions. To defeat these weaknesses and to enhance the overall performances, a new technique is offered building a dynamic multi-swarm design with cooperative rules based on a machine-learning design, namely the hidden Markov classification model. In this approach, a new design with multiple processes implemented inside the PSO that are the control of parameters adaptively with the improvement in the topological structure by setting a multi-swarm layer. Another process of information exchange between swarms is also considered. According to an HMM classification, the entire swarm will be then divided into dynamic cooperating sub-swarms. The size of each sub-swarm is going to be also adjusted at each iteration to suit the search stage. All sub-swarms share information between each other in order to ensure the best exploration of the search space and most effective exploitation. Adaptiveness of both acceleration coefficient and inertia weight strategies is customized with the account of the multi-swarm dynamic evolution and the history of achievements. The approach is simulated and compared by experimental tests to the best-known state of the art.

Economic emission dispatch using an advanced particle swarm optimization technique

Purpose This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems. Design/methodology/approach The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators. Findings The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem. Originality/value The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

Local-global iterative linear embedding optimization-based positioning algorithm in sensor network

ABSTRACT In the distributed iterative positioning algorithm of wireless sensor networks, there will be positioning errors due to the existence of noise and selection of reference nodes; the errors will be further propagated when the nodes selected are taken as the descendant reference nodes. Currently, error propagation is not considered in the distributed iterative algorithm; to reduce the propagation errors, the local linear embedding optimization-based positioning algorithm is proposed in this paper. This algorithm follows the essential criteria of the beacon nodes and takes the mean of the positioned node location and the same node location of the previous generation as the current node location; the particle swarm optimization algorithm of population classification and dynamic learning factor is adopted to accelerate the positioning speed. According to the simulation result, compared with the classical PSO algorithm, the algorithm proposed in this paper has better real-time and effectively improves the positioning accuracy for it has not only saved the positioning time but also effectively reduced the influences of propagation errors on the positioning accuracy.

GENCO Optimal Bidding Strategy and Profit based Unit Commitment using Evolutionary Particle Swarm Optimization Illustrating the Effect of GENCO Market Power

<p>In deregulated electricity markets, generation companies (GENCOs) make unit commitment (UC) decisions based on a profit maximization objective in what is termed profit based unit commitment (PBUC). PBUC is done for the GENCOs demand which is a summation of its bilateral demand and allocations from the spot energy market. While the bilateral demand is known, allocations from the spot energy market depend on the GENCOs bidding strategy. A GENCO thus requires an optimal bidding strategy (OBS) which when combined with a PBUC approach would maximize operating profits. In this paper, a solution of the combined OBS-PBUC problem is presented. An evolutionary particle swarm optimization (EPSO) algorithm is implemented for solving the optimization problem. Simulation results carried out for a test power system with GENCOs of differing market strengths show that the optimal bidding strategy depends on the GENCOs market power. Larger GENCOs with significant market power would typically bid higher to raise market clearing prices while smaller GENCOs would typically bid lower to capture a larger portion of the spot market demand. It is also illustrated that the proposed EPSO algorithm has a better performance in terms of solution quality than the classical PSO algorithm.</p>

Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting

Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedded drawbacks, including trapping in local optima and premature convergence. Hence, to continue exploring possible improvements of the original BA and to receive more appropriate parameters of an SVR model, this paper applies quantum computing mechanism to empower each bat to possess quantum behavior, then, employs the chaotic mapping function to execute the global chaotic disturbance process, to enlarge bat’s search space and to make the bat jump out from the local optima when population is over accumulation. This paper presents a novel load forecasting approach, namely SVRCQBA model, by hybridizing the SVR model with the quantum computing mechanism, chaotic mapping function, and BA, to receive higher forecasting accuracy. The numerical results demonstrate that the proposed SVRCQBA model is superior to other alternative models in terms of forecasting accuracy.

On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach

This article proposes a robust PID adaptive controller for nonlinear systems with one or more degrees of freedom (DoF). The adaptive controller aims at minimizing the errors in trajectory tracking without requiring a prior modeling of the targeted nonlinear system. Furthermore, the proposed controller requires only the inputs and outputs of the system. And it is based on modified particle swarm optimization algorithm whose goal is to find the best PID parameters that optimize the execution of desired task by minimizing an objective function. The adaptation by the controller addresses two critical problems: The first problem is the instability of the control signal provided by the convergence phase of the classical PSO algorithm. This behavior adversely affects the lifetime of any actuator and, therefore, is undesirable. The second problem is the stagnation of the classical PSO algorithm after convergence at the immediately found optimal solution. The proposed adaptive PID controller is initially tested in simulation on a dynamical model of a robot manipulator evolving in the vertical plan. Which is followed by experimental tests performed on an actuated joint orthosis worn by human subjects having different morphologies. A comparative study with two other algorithms has been also conducted. Based on the obtained results, we conclude the efficiency of the proposed approach.

Design of image enhancement filters using a novel parallel particle swarm optimisation algorithm

Designing image enhancement filters with arbitrary frequency response subject to stability constraints is a complex multidimensional optimisation problem. In this paper a novel parallel particle swarm optimisation algorithm (PPSO) is proposed and applied to the design of infinite impulse response image filters. The proposed PPSO consists of two phases: in the first phase, the particle swarm in the classical PSO algorithm is divided into subpopulations that evolve on separate cores of a multi-core machine. Best solutions from each sub population are then interchanged between cores. In the second phase a local search using Nelder-Mead simplex is done to refine the solution. Classical PSO is used for global exploration to explore multiple local minima whereas Nelder-Mead helps refine the solution computed by the PSO. The PPSO outperformed other global optimisation algorithms in terms of the mean square error between the ideal and designed filter frequency responses and CPU usage.

Intellectual Approaches to Improvement of the Classification Decisions Quality on the Base of the SVM Classifier

In this paper the hybrid and modified versions of the PSO algorithm applied to improvement of the search characteristics of the classical PSO algorithm in the development problem of the SVM classifier have been offered and investigated. A herewith two hybrid versions of the PSO algorithm assume the use of the classical “Grid Search” (GS) algorithm and the “Design of Experiment” (DOE) algorithm accordingly, and the modified version of the PSO algorithm realizes the simultaneous search of the kernel function type, the parameters values of the kernel function, and also the regularization parameter value. Besides, the questions of applicability of the k nearest neighbors (kNN) algorithm in the development problem of the SVM classifier have been considered.

Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm

Otsu threshold segmentation is one of the most representative methods for image segmentation. Compared with multilevel threshold segmentation, Otsu method is computationally complex and time-consuming. In this paper, a multilevel thresholding algorithm based on the Quantum Particle Swarm Optimisation QPSO is proposed. QPSO combines the classical PSO algorithm with quantum theory. Because of the high effectiveness of QPSO optimisation algorithm, the paper combines this algorithm with Otsu and uses them in multilevel threshold image segmentation. Experiments show that the algorithm can not only realise the image multilevel threshold segmentation, but also make the segmentation more efficient.

Application of improved PSO-LSSVM on network threat detection

To solve the problem of the design of classifier in network threat detection, we conduct a simulation experiment for the parameters’ optimal on least squares support vector machine (LSSVM) using the classic PSO algorithm, and the experiment shows that uneven distribution of the initial particle swarm exerts a great impact on the results of LSSVM algorithm’s classification. This article proposes an improved PSO-LSSVM algorithm based on Divide-and-Conquer (DCPSO-LSSVM) to split the optimal domain where the parameters of LSSVM are in. It can achieve the purpose of distributing the initial particles uniformly. And using the idea of Divide-and-Conquer, it can split a big problem into multiple sub-problems, thus, completing problems’ modularization. Meanwhile, this paper introduces variation factors to make the particles escape from the local optimum. The results of experiment prove that DCPSO-LSSVM has better effect on classification of network threat detection compared with SVM and classic PSO-LSSVM.

Array Pattern Synthesis Based on the Improved LDW-PSO Algorithm

PSO algorithm is intensively applied in array pattern synthesis. While the choice of inertia factor in classical PSO algorithm is a cumbersome task, improper inertia factor will result in slow convergence or falling into the local minima. This paper presents an improved algorithm based on LDW-PSO to avoid converging to the local minima. Simulation results show that the algorithm has faster speed and higher robustness compared with LDW-PSO algorithm.

A Quantum-Behaved Particle Swarm Algorithm Combined with Chaotic Mutation

The article puts forward an improved PSO algorithm based on the quantum behavior——CMQPSO algorithm to improve premature convergence problem in particle swarm algorithm. The new algorithm first adopts Tent mapping initialization of particle swarm, searches each particle chaos, and strengthens the diversity of searching. Secondly, a method of effective judgment of early stagnation is embedded in the algorithm. Once the early maturity is retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation so as to reduce invalid iteration. The calculation of classical function test shows that the improved algorithm is superior to classical PSO algorithm and quantum-behaved PSO algorithm.

Study of Adaptive Chaos Embedded Particle Swarm Optimization Algorithm

Chaos particle swarm optimization (CPSO) can not guarantee the population multiplicity and the optimized ergodicity, because its algorithm parameters are still random numbers in form. This paper proposes a new adaptive chaos embedded particle swarm optimization (ACEPSO) algorithm that uses chaotic maps to substitute random numbers of the classical PSO algorithm so as to make use of the properties of stochastic and ergodicity in chaotic search and introduces an adaptive inertia weight factor for each particle to adjust its inertia weight factor adaptively in response to its fitness, which can overcome the drawbacks of CPSO algorithm that is easily trapped in local optima. The experiments with complex and Multi-dimensional functions demonstrate that ACEPSO outperforms the original CPSO in the global searching ability and convergence rate.

Adaptive Particle Swarm Optimization for the Design of Three-Phase Induction Motor Considering the Active Power Loss Effect

The paper presents an effective evolutionary method to the optimum design of three-phase induction motor using adaptive particle swarm optimization (APSO) technique. To avoid premature convergence of the classical PSO algorithm, the parameters such as inertia weight factor and acceleration factors are made adaptive on the basis of objective functions of the current and best solutions. The optimization algorithm considers the annual cost of the motor including the power loss cost as objective function and six important motor performance indices as inequality constraints. These functions are expressed in terms of motor design variables. The APSO integrates penalty parameter-less constraint handling strategy for handling the constraints. The potential of the proposed approach has been tested on two sample motors, and the results are compared with genetic algorithm, classical PSO and conventional design methods. It is observed that the proposed method is superior in terms of solution quality, robustness and computational efficiency.

Use of intelligent-particle swarm optimization in electromagnetics

The paper describes a new stochastic heuristic algorithm for global optimization. The new optimization algorithm, called intelligent-particle swarm optimization (IPSO), offers more intelligence to particles by using concepts such as: group experiences, unpleasant memories (tabu to be avoided), local landscape models based on virtual neighbors, and memetic replication of successful behavior parameters. The new individual complexity is amplified at the group level and consequently generates a more efficient optimization procedure. A simplified version of the IPSO algorithm was implemented and compared with the classical PSO algorithm for a simple test function and for the Loney's solenoid.