Integrating Particle Swarm Optimization Research Articles

THE PERFORMANCE COMPARISON OF THREE METAHEURISTIC ALGORITHMS ON THE SIZE, LAYOUT AND TOPOLOGY OPTIMIZATION OF TRUSS STRUCTURES

The structural optimization problem mostly deals with the weight minimization of the structural system. This issue can be assessed from the size, layout and topology aspects. No matter which of these aspects are targeted, to solve them an optimization technique is required. In the last decades the metaheuristic techniques, as the non-gradient optimization algorithms, are widely applied on solving these classes of problems. In the structural optimization, the most time consuming part of the process is the objective function evaluation. Based on this fact, in the current work, these techniques are divided into three main groups as single phase, double phase and multi-phase algorithms. Then based on the author knowledge, three representative methods are picked for each group and their search performance comparatively inspected on solving size, shape and topology optimization of truss structures. To meet this aim, Integrated Particle Swarm Optimization (iPSO), Teaching and Learning Based Optimization (TLBO) and Drosophila Food-Search Optimization (DSO) algorithms are selected, respectively. Different properties like accuracy, convergence rate and complexity of the algorithms are investigated. The outcomes are provided via illustrative diagrams and tables. Based on the achieved results, DSO shows the most complexity level among the other algorithm while the iPSO and TLBO can outperform it on both accuracy and convergence rate. Consequently, iPSO presents a higher accuracy level on finding optimal solutions and TLBO with the lowest standard deviation value through the process shows the highest level of stability on finding optimal solutions.

Fault Detection Analysis Before and After Dynamic Model Reforming on the Benchmark Tennessee Eastman Process

To make the simulation object closer to the actual chemical process, the author improved the model of Tennessee Eastman (TE) process in their previous work. In this paper, based on above improved model, to further analyse its effect on the fault detection performance, a research on fault data of the improved model and original model is done, based on principle component analysis (PCA), the detection rate of Hotelling's T2 statistic, Q statistic and support vector machine (SVM) integrated particle swarm optimization (PSO) approach are used to reflect their detection performance on the two models. From the detection rates, the detection performance gets worse when detecting the fault of the improved model. The analysis indicates that when the detection methods are used to detect the faults in actual chemical process, the detection performance will be influenced and may not be as effective as described in literature.

Security Improvement of AODV Routing Protocol through IPSO-IDRS Mechanism for Ad-hoc Networks

Ad-hoc network is vulnerable to different types of attacks because of its vigorously changing topology, limited storage capacity, absence of centralized infrastructure etc. Ad-hoc On Demand Distance Vector (AODV) is established protocol for routing for this type of networks. AODV is exposed to black hole attack due to the inadequacy of security consideration. The malicious nodes drop all information packets rather than sending it to the neighboring node. In this research paper, Anomaly based intrusion detection system (IDS) known as Particle swarm optimization (PSO) is purposefully utilized to detect the misbehavior activities in the network. In AODV routing protocol, swarm agents are used for identifying misbehavior activities of nodes based on dropping behavior of data packets. An intrusion response system (IRS) is activated after the detection of an intrusion. It is necessary to take action against black hole attack or reduce the effect of the damage caused by the attack. IDS and a response system are integrated for detection and removal of the source of an attack respectively. The guard nodes are placed in the network with the aim to oppose black hole attackers and in this way IR is initiated. The malevolent nodes detected by PSO are bypassed and new routing paths are established using guard nodes. This research has been carried out for analyzing the influence of malicious nodes and guard nodes on varying network size vice versa. The simulation study of proposed technique integrated particle swarm optimization intrusion detection response system (IPSO-IDRS) explains how it is better in terms of the performance metric like throughput and PDR.

Effective hybrid approach for protein structure prediction in a two-dimensional Hydrophobic–Polar model

Hydrophobic–polar (HP) models are widely used to predict protein folding and hydrophobic interactions. Numerous optimization algorithms have been proposed to predict protein folding using the two-dimensional (2D) HP model. However, to obtain an optimal protein structure from the 2D HP model remains challenging. In this study, an algorithm integrating particle swarm optimization (PSO) and Tabu search (TS), named PSO–TS, was proposed to predict protein structures based on the 2D HP model. TS can help PSO to avoid getting trapped in a local optima and thus to remove the limitation of PSO in predicting protein folding by the 2D HP model. In this study, a total of 28 protein sequences were used to evaluate the accuracy of PSO–TS in protein folding prediction. The proposed PSO–TS method was compared with 15 other approaches for predicting short and long protein sequences. Experimental results demonstrated that PSO–TS provides a highly accurate, reproducible, and stabile prediction ability for the protein folding by the 2D HP model.

Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem.

Human Posture Recognition for Estimation of Human Body Condition

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.

Spatial optimization of rural settlement relocation by incorporating inter‐village social connections under future policy scenarios

AbstractSocial connections between villages can represent farmers’ interests and thus benefit participatory rural relocation planning. With rural development, however, these connections will change and may weaken the adaptation of relocation plans to future rural systems. As yet, most studies still use empirical social connections to guide relocation planning, while a few have incorporated predicted connections in the context of rural development into relocation decisions. Meanwhile, spatial optimization approaches have seldom been adopted to solve this geographical decision issue. Accordingly, our study proposes a novel spatial relocation framework that incorporates changed inter‐village social connections under future rural development scenarios. Empirical inter‐village connections and their policy‐induced changes in central China were explored using social network analysis. An integration of particle swarm optimization and geographic information systems was adopted to identify the relocation solutions with maximum inter‐village connections and maximum spatial land use compactness, and to examine how connection changes under different policy scenarios influenced relocation outcomes. The results demonstrate the significance of incorporating policy‐induced social connections into relocation plans, and most importantly, show the negative relations between changed social connections and the migration distance/direction of relocated settlements. Our study is expected to improve the adaptation of relocation plans to future rural development.

Interactive fuzzy search algorithm: A new self-adaptive hybrid optimization algorithm

Current investigation deals with presenting a new hybrid metaheuristic optimization technique which is named as Interactive Fuzzy Search Algorithm (IFSA). Proposed method combines the affirmative features of Integrated Particle Swarm Optimizer (iPSO) and Teaching and Learning Based Optimizer (TLBO) techniques with a fuzzy decision mechanism. Proposed IFSA benefiting its fuzzy module provides a self-adaptive synchronization between local and global search strategies during the optimization process. Since in the proposed approach the tunable parameters are automatically assigned, IFSA acts as an ad-hoc free algorithm. To authenticate the validity of the proposed method, its performance is verified over the number of different types of mathematical, mechanical and structural optimization problems. Achieved outcomes indicate that the introduced algorithm has a high competitive potential on solving different class of optimization problems.

Robot Arm Trajectory Planning Optimization Based on Integration of Particle Swarm Optimization and A* Algorithm

Robot Arm Trajectory Planning Optimization Based on Integration of Particle Swarm Optimization and A* Algorithm

Thermal analysis of parabolic trough collectors via a swarm intelligence optimizer

The current study deals with developing an efficient strategy to solve the governing equations of the parabolic trough collectors’ heat transfer. In the proposed approach, available system of equations is converted into a single objective constrained optimization problem. Subsequently, the thermal properties of the system are gained via solving proposed optimization problem. This target should be met via implementing an appropriate optimization technique as a solver tool. In this respect, integrated particle swarm optimization (iPSO) is utilized as an efficient optimization method. The objective function of the proposed optimization problem computes the heat losses through the discretized cross-section of the receiver tube. To reduce the model complexity, the receiver tube is modeled in one-dimensional form. In addition, the thermal equilibrium of the entire system is implemented as the constraint of the optimization problem. The validity of the obtained outcomes is assessed via comparing them with existing practical results addressed in Sandia National Laboratory (SNL) reports. The comparison reveals that the achieved theoretical outcomes are in the good agreement with the experimental results.

Heuristic optimization techniques for voltage stability enhancement of radial distribution network with simultaneous consideration of network reconfiguration and DG sizing and allocations

In this paper, heuristic optimization techniques, such as integrated particle swarm optimization (IPSO), teaching--learning-based optimization (TLBO), and Jaya optimization, were applied effectively for the first time to optimize the radial distribution network (RDN) by simultaneously considering reconfiguration of the network and allocation and sizing of the distributed generations (DG). The objectives were to maximize the voltage stability and to minimize the power loss of the network without violating the system constraints. In standard PSO technique, the movement of current particle depends upon global best position and its own best position up to current step. However, if the particle lies too close to any of these positions, the guiding role highly decreases and even vanishes. To resolve this problem and to find the global best solution, IPSO was utilized to optimize the network reconfiguration and DG allocation and sizing problem in the RDN. Also, the optimization techniques, such as TLBO and Jaya optimization, which do not require any tuning of parameters, unlike other heuristic optimization techniques, were implemented successfully in this paper. Seven test cases were generated from different combinations of network reconfiguration and DG allocation and sizing. Moreover, for comparison, the optimization techniques, such as particle swarm optimization (PSO), adaptive cuckoo search algorithm (ACSA), harmony search algorithm (HSA), and fireworks algorithm (FWA), were also applied to IEEE 33- and 69-bus distribution test networks. The comparison results prove overall superiority of Jaya optimization when applied on the two IEEE bus systems with seven test cases undertaken.

An integrated particle swarm optimization approach hybridizing a new self-adaptive particle swarm optimization with a modified differential evolution

Hybridizing particle swarm optimization (PSO) with differential evolution (DE), this paper proposes an integrated PSO–DE optimizer and examines the performance of this optimizer. Firstly, a new self-adaptive PSO (SAPSO) is established to guide movements of particles in the proposed hybrid PSO. Aiming at well trade-offing the global and local search capabilities, a self-adaptive strategy is proposed to adaptively update the three main control parameters of particles in SAPSO. Since the performance of PSO heavily relies on its convergence, the convergence of SAPSO is analytically investigated and a convergence-guaranteed parameter selection rule is provided for SAPSO in this study. Subsequently, a modified self-adaptive differential evolution is presented to evolve the personal best positions of particles in the proposed hybrid PSO in order to mitigant the potential stagnation issue. Next, the performance of the proposed method is validated via 25 benchmark test functions and two real-world problems. The simulation results confirm that the proposed method performs significantly better than its peers at a confidence level of 95% over the 25 benchmarks in terms of the solution optimality. Besides, the proposed method outperforms its contenders over the majority of the 25 benchmarks with respect to the search reliability and the convergence speed. Moreover, the computational complexity of the proposed method is comparable with those of some other enhanced PSO–DE methods compared. The simulation results over the two real-world issues reveal that the proposed method dominates its competitors as far as the solution optimality is considered.

Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm

In this investigation a new optimization algorithm named as interactive search algorithm (ISA) is presented. This method is developed through modifying and hybridizing the affirmative features of recently developed integrated particle swarm optimization (iPSO) algorithm with the pairwise knowledge sharing mechanism of the teaching and learning based optimization (TLBO) method. Proposed ISA provides two different navigation schemes as Tracking and Interacting. Each agent based on its tendency factor can pick one of these two schemes for searching the domain. Additionally, ISA utilizes an improved fly-back technique to handle problem constraints. The proposed method is tested on a set of mathematical and structural optimization benchmark problems with discrete and continuous variables. Numerical results indicate that the new algorithm is competitive with other well-stablished metaheuristic algorithms.

Interactive search algorithm: A new hybrid metaheuristic optimization algorithm

In this paper, a new hybrid optimization algorithm, called “Interactive Search Algorithm (ISA)” is proposed for the solution of the optimization problems. This algorithm modifies and combines affirmative features of two developed metaheuristic methods called Integrated Particle Swarm Optimization (iPSO) and Teaching and Learning Based Optimization (TLBO). ISA consists of two separate paradigms: (i) Tracking and (ii) Interacting. Tracking paradigm utilizes the information stored in the current agent’s memory and two other important agents, the weighted and best agents, to guide the colony. On the other hand, interacting paradigm provides a pairwise interaction between agents to share their knowledge with each other. Each agent based on its tendency factor employs one of these two paradigms in each cycle of ISA to explore the search space. Additionally, rather than conventional penalty approach, ISA utilizes the improved fly-back approach to handle problem constraints. The search capability of the proposed method is tested on the number benchmark mathematical functions and constrained mechanical design problems as the real-world examples. Consequently, the achieved numerical results demonstrate that the proposed method is competitive with other well-established metaheuristic methods.

Force control for path following of a 4WS4WD vehicle by the integration of PSO and SMC

ABSTRACTThe aim of this paper is to present a novel control method for a four-wheel steer and four-wheel drive (4WS4WD) vehicle. The novelty is in the integration of sliding mode control (SMC) and particle swarm optimization (PSO) that is proposed to solve the control problem caused by the nonlinear, highly coupled and over-actuated characteristics of the four-wheel steer and four-wheel drive (4WS4WD) vehicle. The validity of the control method is evaluated by two criterions, namely path following performance assessed by the vehicle's position errors with respect to the reference path, and motion quality reflected by the smoothness of vehicle's velocities and accelerations. In vehicle modelling, a kinematic model and a dynamic model considering all slip forces are proposed for the controller design. Simulation results are provided to demonstrate the applicability of the proposed methodology and its robustness.

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Trust Factor and Fuzzy Firefly Integrated Particle Swarm Optimization Based Intrusion Detection and Prevention System for Secure Routing of MANET

Application of Adaptive PSO and Adaptive Fuzzy Logic Controllers to Speed Control PMSM Motor Servo Systems

Three phases Permanent Magnet Synchronous Motors (PMSM) are non-linear resistors, resistance of stator winding, air gap flux, cross-coupling, saturation variable times and cogging torque in operation. Due to the nonlinear nature of PMSM, it is a challenge to control exactly the speed, torque and position. This paper presents two methods for speed control stabilization of the PMSM using the Adaptive Fuzzy Logic - Proportional Integral Derivative controller (AFL-PID) and Adaptive Particle Swarm Optimization - Proportional Integral Derivative controller (APSO-PID). The response results of the speed control PMSM Servo Systems use AFLC-PID and APSO-PID methods are compared and the conclusions are given.

Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation

Software effort estimation (SEE) is the process of forecasting the effort required to develop a new software system, which is critical to the success of software project management and plays a significant role in software management activities. This study examines the potentials of the SEE method by integrating particle swarm optimization (PSO) with the case-based reasoning (CBR) method, where the PSO method is adopted to optimize the weights in weighted CBR. The experiments are implemented based on two datasets of software projects from the Maxwell and Desharnais datasets. The effectiveness of the proposed model is compared with other published results in terms of the performance measures, which are MMRE, Pred(0.25), and MdMRE. Experimental results show that the weighed CBR generates better software effort estimates than the unweighted CBR methods, and PSO-based weighted grey relational grade CBR achieves better performance and robustness in both datasets than other popular methods.