Particle swarm optimization with dynamic search strategies based on landscape modality estimation

Many traditional and Artificial Intelligence techniques have been projected for reactive power dispatch to advance voltage stability of the system. This paper presents particle swarm optimization (PSO) approach for system parameters development and optimal reactive power dispatch. The transformers tap changers, Generator exciters, switchable VAR sources/Static VAR compensators (SVC) are used as control variables for advancement of system parameters and voltage stability. The suggested methodology is based on the minimization of sum of the squares of L-index value at all load buses. The advanced PSO Algorithm is tested on an IEEE Equivalent New England 39-bus system. The performance of PSO is compared with conventional Linear Programming (LP) method and outcomes are presented for illustration purpose. The correctness of voltage stability is measured in terms of system parameters.

In today’s modern world, any community anywhere has the right to have access to a basic level of electricity. It is with this in mind that efforts are being made to provide electric power to even the most remote locations. Solar and wind energy are examples of renewable energy sources that are both clean and versatile. For a distant rural school in south-eastern Iraq, this research presents Particle Swarm Optimization (PSO) to reduce the Cost of Energy (COE) according to maximum dependability of Hybrid Renewable Energy System (HRES), by utilizing an integrated electrical generation system. The suggested hybrid system consists of PV, WT, and BT, all of which are subject to a certain investment restriction. Results showed that, the optimal sizing of the Number of Photovoltaics (NPV) equal to (9), Number of Wind turbines (NWT) equal to (6), Number of the batteries (NBT) of (29), Cost of energy (0.536 US$/kwh), Loss power supply probability (0.091 %), reliability (99.909 %) and renewable factors (100 %) with (59 %) PV penetration, and (41 %) WT penetration. As a result, the use of the hybrid system is justified from a technological, economic, and humanitarian standpoint.

In many real-world optimization problems, multimodal function optimization is considered, in fact, we are faced with a multimodal optimization problem. Particle swarm optimization (PSO) and continuous ant colony optimization (ACOR) are two population-based optimization techniques that work based on probability laws. The main problem of PSO and ACOR algorithms is premature convergence and falling into local optima. One way to solve the problems is to use combinational methods. This paper presents a combinational method including PSO and ACOR in order to improve the search process. The proposed algorithm tries to solve the problem. Standard benchmark functions are used in order to evaluate the proposed algorithm, proposed method was compared with ACOR, PSO and sequential approach with the enlarged pheromone-particle table of the composition of PSO and ACOR Introduced in [8]. Results show that the proposed method is superior.

An improved differential evolution (DE) method based on the dynamic search strategy (IDEBDSS) is proposed to solve dynamic economic dispatch problem with valve-point effects in this paper. The proposed method combines the DE algorithm with the dynamic search strategy, which improves the performance of the algorithm. DE is the main optimizer in the method proposed. While chaotic sequences are applied to obtain the dynamic parameter settings in DE, dynamic search strategy which consists of two steps, global search strategy and local search strategy, is used to improve algorithm efficiency. To accelerate convergence, a new infeasible solution handing method is adopted in the local search strategy; meanwhile, an orthogonal crossover (OX) operator is added to the global search strategy to enhance the optimization search ability. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by three test systems, and the simulation results reveal that the IDEBDSS method can obtain better solutions with higher efficiency than the standard DE and other methods reported in the recent literature.

In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The influences of self-localisation and communication errors and limited travel distance of the autonomous underwater vehicles (AUVs) on the performance of the proposed algorithms are investigated. This study shows that the proposed search strategies could not outperform the classic search heuristic based on full coverage path planning if all AUVs followed the same search strategy. In this study, the influence of self-localisation and communication errors was investigated. The simulations showed that, based on the median error of the search runs, the performance of SOMA was in the same order of magnitude regardless the strength of the localisation error. Furthermore, it was shown that the performance of BB was highly affected by increasing localisation errors. From the simulations, it was revealed that all the algorithms, except for PSO and SOMA, were unaffected by disturbed communications. Here, the best performance was shown by PPP, followed by BB, SOMA, ILF, PSO, and RW. Furthermore, the influence of the limited travel distances of the AUVs on the search performance was evaluated. It was shown that all the algorithms, except for PSO, were affected by the shorter maximum travel distances of the AUVs. The performance of PPP increased with increasing maximum travel distances. However, for maximum travel distances > 1800 m the median error appeared constant. The effect of shorter travel distances on SOMA was smaller than on PPP. For maximum travel distances < 1200 m, SOMA outperformed all other strategies. In addition, it can be observed that only BB showed better performances for shorter travel distances than for longer ones. On the other hand, with different search strategies for each AUV, the search performance of the whole swarm can be improved by incorporating population-based search strategies such as PSO and SOMA within the PPP scheme. The best performance was achieved for the combination of two AUVs following PPP, while the third AUV utilised PSO. The best fitness of this combination was 15.9. This fitness was 26.4% better than the performance of PPP, which was 20.4 on average. In addition, a novel mechanism for dynamically selecting a search strategy for an AUV is proposed. This mechanism is based on fuzzy logic. This dynamic approach is able to perform at least as well as PPP and SOMA for different travel distances of AUVs. However, due to the better adaptation to the current situation, the overall performance, calculated based on the fitness achieved for different maximum travel distances, the proposed dynamic search strategy selection performed 32.8% better than PPP and 34.0% better than SOMA.

Optimization Based Support Vector Classifier for Vibration Fault Diagnosis of Steam Turbine-Generator Sets

The idea of particle swarm optimization falls under the domain of swarm intelligence. Particle swarm optimization technique is widely used for finding the global minima of well-known benchmark functions. The main idea behind this technique is that working in a group improves the performance of a system. A modified particle swarm optimization technique is proposed in this paper and tested on seven standard benchmark functions. The two major modifications are introduced in the standard particle swarm optimization, modify the velocity of a particle such that the particle remains within the confine limits of clamp velocity, and penalize the particle velocity, if the sum of the velocity vector and position vector results in breaching the boundary limits of search space. The results of the modified PSO are compared with the two versions of standard PSO, constant inertial weight with no velocity clamping and linearly decreasing inertial weight with no velocity clamping.

The particle swarm optimization algorithm includes three vectors associated with each particle: inertia, personal, and social influence vectors. The personal and social influence vectors are typically multiplied by random diagonal matrices (often referred to as random vectors) resulting in changes in their lengths and directions. This multiplication, in turn, influences the variation of the particles in the swarm. In this paper we examine several issues associated with the multiplication of personal and social influence vectors by such random matrices, these include: (1) Uncontrollable changes in the length and direction of these vectors resulting in delay in convergence or attraction to locations far from quality solutions in some situations (2) Weak direction alternation for the vectors that are aligned closely to coordinate axes resulting in preventing the swarm from further improvement in some situations, and (3) limitation in particle movement to one orthant resulting in premature convergence in some situations. To overcome these issues, we use randomly generated rotation matrices (rather than the random diagonal matrices) in the velocity updating rule of the particle swarm optimizer. This approach makes it possible to control the impact of the random components (i.e. the random matrices) on the direction and length of personal and social influence vectors separately. As a result, all the above mentioned issues are effectively addressed. We propose to use the Euclidean rotation matrices for rotation because it preserves the length of the vectors during rotation, which makes it easier to control the effects of the randomness on the direction and length of vectors. The direction of the Euclidean matrices is generated randomly by a normal distribution. The mean and variance of the distribution are investigated in detail for different algorithms and different numbers of dimensions. Also, an adaptive approach for the variance of the normal distribution is proposed which is independent from the algorithm and the number of dimensions. The method is adjoined to several particle swarm optimization variants. It is tested on 18 standard optimization benchmark functions in 10, 30 and 60 dimensional spaces. Experimental results show that the proposed method can significantly improve the performance of several types of particle swarm optimization algorithms in terms of convergence speed and solution quality.

Memetic Algorithm (MA) is a metaheuristic search method. In proposed memetic algorithm two memes are used in definite proportion out of total local calls and are applied with random selection. Genetic Algorithm due to its good exploration capability is used as main algorithm and Particle Swarm Optimization (PSO) as well as chemotaxis mechanism of Bacterial Foraging Optimization (BFO) are used as local searches. The memetic process is realized using global best fitness among particles of PSO and by imitating the nutrient information from the bacteria of the best fitness in BFO. The proposed variant of memetic algorithm is tested on the standard benchmark functions with unimodal and multimodal property. When the results are compared, the proposed memetic algorithm shows better performance than MA using PSO (pMA) and MA using BFO (bMA) both in terms of speed of convergence and quality of solutions.

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

Non-convex optimization problems are prevalent in scientific and engineering applications, often characterized by multiple local minima that challenge standard optimization techniques. Global optimization methods, such as Genetic Algorithms (GA) (1992) [4] and Particle Swarm Optimization (PSO) (1995) [6], provide robust global exploration but tend to converge slowly. Conversely, local search methods, such as Newton’s method (2004) [1], (2006) [7], and the Conjugate Gradient (CG) (1964) [3] method, offer rapid convergence but are susceptible to local minima. In this paper, we present four hybrid optimization algorithms that integrate the exploratory strength of global algorithms with the refinement ability of local methods. The proposed hybrids, Genetic Algorithms (GA) + Newton, GA + CG, PSO + Newton, and PSO + CG, are evaluated against their standalone counterparts on standard benchmark functions (2013) [5], including Rosenbrock, Rastrigin, Ackley, and Himmelblau. This work builds upon our previous research on the hybridization of two global search algorithms, the Nelder–Mead (simplex) algorithm and the Bat algorithm, which resulted in the Hybrid Simplex Bat Algorithm (HSBA) [2], recently Published. Results demonstrate that the hybrid approaches consistently outperform traditional methods in terms of accuracy, convergence rate, and computational efficiency.

Parallel Global Best-Worst Particle Swarm Optimization Algorithm for solving optimization problems

Attraction towards Particle Swarm Optimization (PSO) algorithm has grown rapidly in the recent times due to being a simple and efficient optimization technique able to solve many continuous multimodal and multidimensional problems. In this paper, we have attempted to realize the PSO algorithm in Xilinx™ Vertex V FPGA (Field Programmable Gate Array). Standard benchmark functions such as Sphere, Rastrigin and Rosenbrock are considered to investigate the efficiency of the implementation. We have studied the effect of diverse swarm size to evaluate the performance of the PSO algorithm. IEEE 754 double precision floating point format is used to implement arithmetic modules. The results obtained in this work are compared with those reported in literature. Synthesis and simulation results demonstrate that FPGA implementation proposed in this paper converges faster compared to earlier reported work.

Many real-world optimization problems can be solved very efficiently by evolutionary algorithms. There exist many solutions in this field to solve multi-dimensional optimization problems, which is an important issue in today’s world. Swarm-based algorithms are very popular to solve these types of problems because of its effectiveness and quality of solutions. All swarm-based algorithms have different advantages and disadvantages. This paper has discussed two such mostly used algorithms, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC). A fair comparison has been done between these two algorithms by implementing those to minimize five different standard benchmark functions. Results have been recorded and the performance of both the algorithms is analyzed with corresponding to the quality of solutions. It has been observed from the simulation results that ABC outperforms PSO in most of the cases corresponding to solution quality but it requires more time to converge than PSO.

Development and application of Quantum Entanglement inspired Particle Swarm Optimization