High performance data processing of distributed database and multi-core processor based on particle swarm optimization

Particle swarm optimization (PSO) is a stochastic population-based algorithm which was originally introduced by Kennedy and Eberhart [1]. This optimization algorithm is motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. As in most of the metaheuristic optimization algorithms, in PSO, a population of individuals, known as particles, are evolved through successive iterations. The most important advantages of PSO, compared to other optimization strategies, are its easy implementation and few parameters requiring adjustment. Since the initial development of PSO by Kennedy and Eberhart, several variants of this algorithm have been proposed by researchers. The first modification was introducing an inertia weight parameter in the velocity update equation of the initial PSO-a PSO model which is now accepted as the global best PSO algorithm [2]. The goal of the inertia weight parameter is to balance the exploration and exploitation characteristics of PSO. Generally, large inertia weight values are expected to increase the velocity of the particles and improve the long-range exploration of the PSO algorithm, while low inertia values increase the short-range exploration. Due to the strong effect of the inertia weight on the performance of PSO, many researchers have investigated different inertia weight control approaches during the past decades, and many different strategies have been proposed. Various inertia weighting strategies can be categorized into three main classes (Figure 7.1): (1) constant or random, (2) time-varying, and (3) adaptive inertia weights. The first class contains strategies in which the value of the inertia weight is constant during the search or is determined randomly. In the second class, the inertia weight is defined as a function of time (iteration number), and hence these strategies are referred to as time-varying inertia weight strategies. These methods are not considered adaptive since they do not monitor the situation of the particles in the search space, and the value of the inertia weight in each iteration is known before the execution of the algorithm. The third class of the inertia weight strategies consists of those methods which use a feedback parameter to monitor the state of the algorithm and adjust the value of the inertia weight based on the feedback parameter's value. This chapter presents the major existing inertia weight control strategies in the above three categories and discusses examples of each category in detail. The remainder of this chapter is organized as follows. The next section gives a short review of the PSO algorithm, and the role ofthe inertia weight parameter in the velocity update equation. Sections 7.2-7.4 review the three groups of inertia weight strategies, constant or random, time-varying, and adaptive models. Section 7.5 reports some experimental evaluations of different inertia weight models. Finally, Section 7.6 concludes this chapter. A short description of the symbols frequently used in this chapter is shown in Table 7.1.

Inertia weight is one of the most important adjustable parameter of particle swarm optimization (PSO). The proper selection of inertia weight can prove a right balance between global search and local search. In this paper, two novel PSOs with non-linear inertia weight based on the tangent function and the arc tangent function are provided, respectively. The performance of the proposed PSO model is compared with standard PSO with linearly-decrease inertia weight. The experimental results demonstrated that our proposed PSO model is better than standard PSO in terms of convergence rate and solution precision.

Particle Swarm Optimization (PSO) is a new metaheuristic algorithm based on the social behavior of animals. PSO is widely growing and accepted among researchers to find the optimum solution in very large space with low computational complexity. There are various controlled parameters in PSO,and inertia weight (IW) is one of them. An appropriate strategy for varying inertia weight improves the PSOperformance. Recent research works related to varying inertia weight strategy considered small values of w, generally between 0 and 1.The aim of this research paper is to investigate existing varying inertia weight strategy, their effect on PSO performance and to design the new variant of IW for better performance. The same strategy have been implemented in two different proposed inertia weight variants of PSO, namely Modified Simple Random Inertia Weight (MSRIW), and Modified Oscillating Inertia Weight (MOIW). We compared the performance of proposed variants with different existing inertia weight variants of PSO. The proposed strategy is better than existing in term of convergence speed. Further this strategy can be implemented for task scheduling in cloud computing for better performance.

ABSTRACTOne of the most familiar stochastic heuristic search algorithm is Particle swarm optimization (PSO), which is motivated by social behavior of animals like birds, fishes, and so forth. The significant advantages of PSO algorithm are simple structure and limited parameters to be used. Among the parameters, inertia weight is considered as the most crucial one in PSO which brings trade-off between the characteristics of exploitation and exploration. A novel Interactive Self-Improvement based Adaptive PSO (ISI-APSO) method that traits better searching efficiency and accuracy than the traditional particle swarm optimization is proposed. More precisely, it can achieve faster convergence speed while on global search over the entire search space. The simulation results show that the performance of our proposed ISI-APSO is substantially improved than other heuristic algorithms in terms of the search efficiency and convergence speed.

Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function

This paper describes the evaluation of the spreading factor inertia weight Particle Swarm Optimization (PSO) for the fuzzy logic control (FLC) of FES-assisted paraplegic indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of lower extremities through the application of functional electrical stimulation (FES). FLC is used to control the knee trajectories for smooth rowing manoeuvre and minimize the total electrical stimulation required by the muscles. PSO is implemented to optimize the parameter of the FLC. The objective function specified is to minimize the mean squared error of knee angle trajectory. The inertia weight of the PSO is updated using spreading factor technique and it performance is compared to the performance of PSO with time variant inertia weight. In view of good results obtained, it is concluded that Spreading Factor Inertia weight PSO is able to obtain the optimal parameter for FLC of FES-rowing.

Unmanned Aerial Vehicle (UAV) path planning is a critical challenge in autonomous navigation, requiring intelligent trajectory generation in complex environments that balance safety, efficiency, and adaptability. Particle Swarm Optimization (PSO), a prominent swarm intelligence algorithm, is widely used for its robust global search capabilities. However, PSO’s performance heavily depends on parameter configurations, particularly the inertia weight (ω). This paper investigates the impact of parameter settings on PSO-based UAV path planning and proposes optimization strategies to enhance its efficacy.Simulation experiments reveal that when ω = 0.5, PSO exhibits strong local search capabilities and fast convergence but is prone to local optima, reducing path planning precision. Increasing ω enhances global search, enabling broader solution space exploration, but at the cost of slower convergence and limited optimization gains due to computational constraints. Notably, when ω = 1.5, the algorithm avoids local optima but shows minimal performance improvement and reduced efficiency.The study also evaluates optimization strategies, including a linearly varying inertia weight and a mutant particle strategy. The linearly varying inertia weight significantly improves optimization performance but slows convergence, making it suitable for high-accuracy scenarios with ample computational resources. The mutant particle strategy, however, achieves a better balance between optimization and convergence, ideal for practical applications requiring both efficiency and accuracy. Combining these strategies does not yield substantial improvements, suggesting that a single strategy may be more computationally efficient.This research provides valuable insights into PSO parameter configuration and optimization for UAV path planning, advancing the field of autonomous navigation technology.

Particle swarm optimization (PSO) is one of the modern heuristic algorithms. PSO has attracted great attention due to its features of easy implementation, robustness to control parameters and computation efficiency compared with other existing heuristic algorithms. The performance of a PSO can depend on its parameters such as the inertia weight factors and two acceleration coefficients. The value of inertia weight can treat the balance between exploration and exploitation, so a proper control of inertia weight is very important to find the optimum solution efficiently. This paper compared several kinds of inertia weights. Experimental results demonstrate that simulated annealing inertia weight and linearly decreasing inertia weight have better convergence performance in the searching procedure. The dimension of the solution has no effect on the performance of inertia weight. The performance of the two kinds of inertia weight will change when the benchmark functions and acceleration coefficients change.

This paper attempts to substitute Resource Leveling Problem (RLP) into multi-project environment and construct Resource Leveling Problem of Multi-project (RLPMP) model with the goal of minimizing the sum of weighted mean square deviations of multi-resource requirements. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist reservation (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of Particle Swarm Optimization (PSO) algorithm with dynamic inertia weight. Through the dynamic change of inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can effectively solve the RLPMP model, and then effectively improve the balance of multi-project resources.

Particle Swarm Optimization (PSO) is an evolution-nary computation technique. Separate adjustment to inertia weight and learning factors in PSO undermines the integrity and intelligent characteristic in the evolutionary process of particle swarm to some extent, thus it is not suitable for solving most complicated optimization problems. On the basis of previous researches, the aim of this study was to improve the computational efficiency of PSO and avoid premature convergence for multimodal, higher dimensional complicated optimization problems by considering the mutual influences of inertia weight and learning factors on the updates of particle's veloci-ties. A great number of experiment data provided evidence that the nonlinear adjustment to inertia weight, cognitive learning factor and social learning factor within a certain interval is a good selection. Moreover, the simulation results on four typical functions show that the improved strategy of PSO proposed in the paper is available for solving multimodal and higher dimensional complicated optimization problems, and can accelerate convergence speed, improve optimization quality effectively in comparison to the algorithms of standard PSO and existing relevant improved PSO.

This paper presents performance comparative study of various particle swarm optimization (PSO) techniques for the placement of generator units in the distributed generation (DG) system. For the installation of generator units in the distributed generation system, it is very important to know the generator sizing and its placement in the network system for reducing the line losses and hence the cost. Various PSO techniques such as Canonical PSO, Hierarchical PSO (HPSO), Time varying acceleration coefficient (TVAC) PSO, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), Stochastic inertia weight (Sto-IW) PSO and Time varying inertia weight (TVIW) PSO have been used for comparative study. Here the main objective function (OF) is to minimize the system cost. These techniques have been tested on the standard IEEE-14 bus, IEEE-30 bus and IEEE-57 bus network system by the use of MATLAB software.

Active contour models, colloquially known as snakes, are quite popular for several applications such as object boundary detection, image segmentation, object tracking and classification. A snake is an energy minimizing curve that, starting from an initial contour, deforms iteratively thereby gradually moving towards the desired object boundary. Finally, it shrinks and wraps around the object. Therefore, the problem at hand is to find the contour that minimizes the snake energy. While energy minimization may be accomplished using traditional optimization methods, approaches based on nature-inspired evolutionary algorithms have been developed in recent years. One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization (PSO). However, conventional PSO converges slowly and gets trapped in local minimum easily. The problem of local minimum results in inaccurate detection of concavities in the object boundary. This is taken care of by using multi-swarm PSO in which a swarm is set for every control point in the snake and then all the swarms search for their best points simultaneously and collaboratively through information sharing among them. The performance of the search process is further enhanced by using dynamic adaptation of the inertia factor. Inertia factor is used to balance the global and local search ability. In this paper, we propose to use a set of fuzzy rules to adjust the inertia weight on the basis of the current normalized snake energy and the current value of inertia. This improves the search for object concavities. Experimental results demonstrate the effectiveness of the proposed method compared to conventional approaches.

A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydro thermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms(GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.

A novel methodology for simultaneous parametric fault isolation and mode switching identification in the spool motion of a Directional Control Valve (DCV), under multi fault assumption, has been reported in this paper. The shape of the profile traversed by the DCV spool was assumed to be trapezoidal in both healthy and faulty condition, but the slope of trapezoidal may change due to fault. Under this assumption, the real valued fault parameter and binary mode switching were identified by real valued Particle Swarm Optimization (PSO) alone instead of a combined real and binary valued PSO (Hybrid PSO). A novel PSO algorithm by combining the concepts of varying inertia weight (both increasing and decreasing trend) and constriction factor has been proposed in the article to achieve more accurate identification. Its validity was examined using an existing heuristic formula and by conducting several tests on a benchmark function used for fault identification. Superior improvement was observed in the identification with increasing inertia weight than that of widely used decreasing inertia weight, when combined with the constriction coefficient. A high pressure hydraulic circuit used in dumper and several other lifting machines was used as a simulation example.

High-precision wireless localization has attractive application prospects. Cooperative localization is an effective tool to improve localization accuracy. However, compared with non-cooperative localization, in cooperative localization networks, large-scale neighboring links and nonlinear measurement functions cause the associated objective function to be non-convex. It is difficult to obtain global optimum using classical particle swarm optimization (PSO) algorithm or analytical methods. In order to solve this problem, a classified particle swarm optimization (CPSO) algorithm is proposed in this paper. For classical PSO, all search particles have the same inertial weight and learning factor. Unlike classical PSO, the proposed CPSO algorithm classified different search particles based on particle cost value and set different inertial weights and learning factors for search particles. Meanwhile, considering the unavoidable reference node location error, localization result could be achieved by calculating the weighted average of close-range particle locations. Simulation results prove that the CPSO algorithm improves positioning accuracy by 25.3% compared with classical PSO algorithm.