Well-known Particle Swarm Optimization Algorithm Research Articles

A modified Particle Swarm Optimization algorithm with enhanced search quality and population using Hummingbird Flight patterns

This study proposes a modified Particle Swarm Optimization (PSO) algorithm based on Hummingbird Flight (HBF) patterns to enhance the search quality and population diversity. The HBF has five concepts: (1) Smaller steps toward position updating are more likely than larger ones, (2) Position changes are made step by step throughout the flight, (3) Flight energy is conserved during the nectar-searching process, (4) Hummingbirds do not fly in large groups in confined spaces, and (5) Simultaneous position changes in all directions are not realistic. A comprehensive study on two CEC-2010 and CEC-2013 benchmark suites is conducted to verify the effectiveness of the proposed PSO-HBF algorithm. The proposed algorithm is also evaluated and compared to other well-known PSO algorithms using shifted and rotated CEC 2005 and CEC 2014 benchmark functions. Four cases in economic dispatch, the 10-unit reserve constraint, and the 30-unit dynamic economic dispatch (DED) are further examined. The last two cases investigate how the proposed PSO-HBF deals with large-scale practical problems. The results demonstrated that the PSO-HBF algorithm is superior to seven other modified algorithms, improving eight and ten functions on the 2010 and 2013 benchmarks, respectively. Furthermore, achieving the third rank among the nineteen improved PSO algorithms based on the 2005 functions confirms the effectiveness of the proposed algorithm. Moreover, in two cases of the DED problem, the results of PSO-HBF show significant improvement over previously published papers. The PSO-HBF algorithm’s source code can be accessed publicly at http://www.optim-app.com/projects/psohbf.

Spectral Richness PSO algorithm for parameter identification of dynamical systems under non-ideal excitation conditions

This work proposes a new Particle Swarm Optimization (PSO) algorithm specifically designed for parameter identification of physical systems. The key feature of the proposed algorithm is that it takes into consideration the Spectral Richness of the signal used for exciting the system during the identification procedure. The Spectral Richness is essentially the number of spectral lines in the Fast Fourier Transform of a signal, and if this number is large, the variability of the parameter estimates produced by well-known PSO algorithms is small. However, low values of Spectral Richness may cause large variability of the parameter estimates. The proposed approach, which is termed as the Spectral Richness Particle Swarm Optimization (SR-PSO) algorithm, reduces the variability of the parameter estimates when using an excitation signal with a low value of Spectral Richness compared with state-of-the-art PSO algorithms, and maintains the same performance if the Spectral Richness is high. In a first case study, a set of real-time experiments applying excitation signals with different levels of Spectral Richness have been performed on a servomechanism in order to obtain several data sets. The acquired data from each excitation signal has been used to perform a parameter identification process using various algorithms including the classic PSO, the Linear Variable Weight Particle Swarm Optimization (LVW-PSO), the Linear Variable Constriction Factor Particle Swarm Optimization (LVCF-PSO), the state-of-the-art Fractional Order Regulated Particle Swarm Optimization (FOR-PSO) and the proposed Spectral Richness Particle Swarm Optimization (SR-PSO). The pertinence of the parameter estimates is also evaluated by designing and experimentally applying a model-based feedback control to the servomechanism.The proposed SR-PSO algorithm is also tested in a thermoelectric cooler model using numerical simulations. It is shown that the values of the identified parameters heavily depends on the kind of excitation signal applied to this system.

DDAPSO: Hybrid Discrete Dragonfly Algorithm and Particle Swarm Algorithm to Service Selection and Composition for the Internet of Things Applications

Recently, the Internet of Things (IoT) has quickly risen as one of the most essential technologies of this century. IoT allows users to connect to a vast network of smart devices, services, and data. An important and challenging research problem in the Internet of Things applications is how to select an appropriate service selection (SS). In the SS problem, users can combine several services from diverse sources (things or devices) to satisfy their needs. On the other hand, the SS problem is known for its complexity and is categorized as an NP-hard problem; such problems are typically solved utilizing heuristics like bio-inspired algorithms. In this research a new bio-inspired algorithm called DDAPSO is created to solve the SS problem where a new strategy is proposed to maintain a balance between the exploration and exploitation abilities. This hybrid algorithm is the result of coupling a Discrete Dragonfly Algorithm (DDA) with the particle swarm optimization algorithm (PSO). The suggested algorithm was properly tested using a variety of scenarios with different numbers of services and with different numbers of concrete services per each service set or task. The proposed algorithm is compared with the main recent well-known algorithms, i.e. GA, PSO, DDA, ABC and MVO for service selection. In a large-scale setting, the results clearly show that the DDAPSO algorithm outperforms other services selection algorithms reported in the literature in terms of selection optimally as well as execution time.

A multi-sample particle swarm optimization algorithm based on electric field force.

Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.

An Efficient Particle Swarm Optimization with Multidimensional Mean Learning

Particle swarm optimization (PSO) algorithm is a stochastic and population-based optimization algorithm. Its traditional learning strategy is implemented by updating the best position using the particle’s own historical best experience and its neighborhood’s best experience to find the optimal solution of the problem. However, the learning strategy is ineffective when dealing with highly complex problems. In this paper, a particle swarm optimization algorithm based on a multidimensional mean learning strategy is proposed. In this algorithm, an opposition-based learning strategy is utilized to initialize the population to enhance the exploitation capability. Furthermore, the historical best positions of all the particles are reconstructed in a vertical crossover manner that is based on the mean information of multiple optimal dimensions to generate the guiding particles. Additionally, an improved inertia weight is used to further guide all the particle movements to balance the capability of the proposed algorithm for global exploration and local exploitation. The proposed algorithm is tested on 12 benchmark functions and is compared with some well-known PSO algorithms. The experimental results show that the proposed algorithm obtains more competitive optimal solution compared with other PSO algorithms when solving high-dimensional complex problems.

A new hierarchical multi group particle swarm optimization with different task allocations inspired by holonic multi agent systems

Nowadays expert systems have been used in different fields. They must be able to operate as quickly and efficiently as possible. So, they need optimization mechanism in their different parts and optimization is a critical part of almost all expert systems. Because of difficulties in real world problems, traditional optimization techniques commonly cannot solve them. Therefore, stochastic algorithms are used to do the optimization in expert systems. Particle swarm optimization (PSO) is one the most famous stochastic optimization algorithms. But this algorithm has some difficulties like losing diversity, premature convergence, trapping in local optimums and imbalance between exploration and exploitation. To overcome these drawbacks, inspired by holonic organization in multi agent systems, a new hierarchical multi group structure for PSO is presented in this paper. Considering the particles in PSO as simple agents, PSO is a kind of multi agent system. Existence of different facilities and organizations in multi agent systems and their great impact on performance encouraged us to use them. So, inspired by holonic multi agent systems, a new structure for PSO is presented. This work has been done for the first time in the literature. Meanwhile, to promote exploration and exploitation ability of proposed structure and create a suitable balance between them, different tasks are assigned to different groups of this structure. So, a holonic PSO with different task allocations (HPSO-DTA) is created. It provides the opportunity to employ all aspects for empowering PSO including parameter settings, neighborhood topologies and learning strategies to enhance the ability of it unlike other versions of PSO that use only one of these aspects to improve their solutions. This structure provides a lot of advantages for PSO. It is a new topological structure that improves the performance of PSO. It provides several leaders with efficient information to guide the particles in the search space. Also, it helps to control suitable information flow between groups and particles in order to preserve diversity and prevent from trapping in local optimums. Meanwhile, with assigning different tasks to different groups of proposed structure, an appropriate balance between exploration and exploitation is created to enhance the performance of the algorithm. In each group, based on its assigned task, particles use different parameters settings, different dynamic neighborhood topologies and different learning strategies which are proposed in this paper to enhance the performance of algorithm. A set of thirty four benchmark functions are used to evaluate the performance of proposed structure. Proposed algorithm is compared with a set of well-known PSO algorithms that their efficiency have been proved. Experimental results and comparative analysis demonstrate good performance of HPSO-DTA compared to other algorithms. Its solution accuracy, convergence speed and robustness is completely appropriate especially in more complicated benchmarks.

Exploiting flower constancy in flower pollination algorithm: improved biotic flower pollination algorithm and its experimental evaluation

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena—called flower constancy—the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented.

Automatic Data Clustering Using Hybrid Firefly Particle Swarm Optimization Algorithm

The firefly algorithm is a nature-inspired metaheuristic optimization algorithm that has become an important tool for solving most of the toughest optimization problems in almost all areas of global optimization and engineering practices. However, as with other metaheuristic algorithms, the performance of the firefly algorithm depends on adequate parameter tuning. In addition, its diversification as a global metaheuristic can lead to reduced speed, as well as an associated decrease in the rate of convergence when applied to solve problems with large number of variables such as data clustering problems. Clustering is an unsupervised data analysis technique used for identifying homogeneous groups of objects based on the values of their attributes. To mitigate the aforementioned drawbacks, an improved firefly algorithm is hybridized with the well-known particle swarm optimization algorithm to solve automatic data clustering problems. To investigate the performance of the proposed hybrid algorithm, it is compared with four popular metaheuristic methods from literature using twelve standard datasets from the UCI Machine Learning Repository and the two moons dataset. The extensive computational experiments and results analysis carried out shows that the proposed algorithm not only achieves superior performance over the standard firefly and particle swarm optimization algorithms, but also exhibits high level of stability and can be efficiently utilized to solve other clustering problems with high dimensionality.

A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario

UAV networks have been in the spotlight of the research community on the last decade. One of the civil applications in which UAV networks may have more potential is in emergency response operations. Having a UAV network that is able to deploy autonomously and provide communication services in a disaster scenario would be very helpful for both victims and first responders. However, generating exploratory trajectories for these networks is one of the main issues when dealing with complex scenarios. We propose an algorithm based on the well-known Particle Swarm Optimization algorithm, in which the UAV team follows the networking approach known as Delay Tolerant Network. We pursue two main goals, the first is exploring a disaster scenario area, and the second is making the UAVs converge to several victims groups discovered during the exploration phase. We have run extensive simulations for performing a characterization of the proposed algorithm. Both goals of the mission are successfully achieved with the proposed algorithm. Besides, in comparison to an optimal trajectory planning algorithm that sweeps the entire disaster scenario, our algorithm is able to discover faster the 25%, 50% and 75% of the scenario victims and it converges faster. In addition, in terms of connections events between a victim and a UAV, our algorithm shows more frequent connections and less time between consecutive connections.

Optimally sized design of a wind/photovoltaic/fuel cell off-grid hybrid energy system by modified-gray wolf optimization algorithm

This study deals with an off-grid hybrid energy generation system composed of wind turbines, photovoltaic cells, and fuel cells to supply a specific load. The purpose is to minimize the cost of energy generation over a period of lifetime while satisfying a set of system reliability constraints in the system. The stated objective is to determine the optimal value of system components, that is, the number of wind turbines, the number and angle of photovoltaic arrays, the size of electrolyzer, hydrogen tanks, fuel cells, and DC/AC converters. The costs incorporated into this design included net present value of investment, costs of equipment, replacement and maintenance, and the costs arising from power supply interruption, all for a period of 20 years considered as the system lifetime. The data pertaining to load demand, sunlight and wind speed were considered to be known and deterministic. This design considered the failure of three main system components, namely, wind turbines, photovoltaic cells, and AC/DC converter, and incorporated a number of cost factors such as initial investment, operating and maintenance expenses, and value of lost load. The wind and solar data used in this study pertained to Ankara, Turkey. The gray wolf optimization algorithm for the first time is used to optimize such a system, and the results are compared with the ones obtained by particle swarm optimization algorithm. A new hybrid metaheuristic algorithm based on the modified-gray wolf optimization algorithm and the traditional particle swarm optimization algorithms is proposed to solve the problem. The results indicate that the gray wolf optimization algorithm achieves better optimal results in comparison to the well-known particle swarm optimization algorithm and the developed hybrid method performs better in comparison to the gray wolf optimization and particle swarm optimization algorithms for this specific optimization problem.

A stochastic well-test analysis on transient pressure data using iterative ensemble Kalman filter

Accurate estimation of the reservoir parameters is crucial to predict the future reservoir behavior. Well testing is a dynamic method used to estimate the petro-physical reservoir parameters through imposing a rate disturbance at the wellhead and recording the pressure data in the wellbore. However, an accurate estimation of the reservoir parameters from well-test data is vulnerable to the noise at the recorded data, the non-uniqueness of the obtained match, and the accuracy of the optimization algorithm. Different stochastic optimization methods have been applied to this address problem in the literature. In this study, we apply the recently developed iterative ensemble Kalman filter in the context of well-test analysis to infer reservoir parameters from the noisy recorded data. Since the introduction of the ensemble Kalman filter (EnKF) by Evensen in 1994 as a novel method for data assimilation, it has had enormous impact in many application domains because of its robustness and ease of implementation, and numerical evidence of its accuracy. While the objective of the standard EnKF approaches is to approximate the statistical properties of geological parameters conditioned to observation, via an ensemble, the objective of the iterative ensemble Kalman methods is to approximate the solution of inverse problems using a deterministic derivative-free iterative scheme. We conducted three case studies of the application of the iterative ensemble Kalman methods for a well-test analysis of a homogenous reservoir model, a dual-porosity heterogeneous system, and a faulted discontinuous reservoir. We demonstrated that the convergence occurs very rapidly almost at the first iterations contrary to the well-known particle swarm optimization algorithm. The maximum relative error for the simulated cases is below 15%, which belongs to the skin factor. Low relative error, narrowed uncertainty range over time, and excellent graphical match obtained between the simulated derivative data and the generated curve by using the iterative EnKF verify the robustness of the developed algorithm even in dealing with complex heterogeneous models.

Path planning for intelligent robot based on switching local evolutionary PSO algorithm

Purpose This paper aims to present a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for path planning of intelligent robot when having obstacles in the environment. Design/methodology/approach The three-dimensional path surface of the intelligent robot is decomposed into a two-dimensional plane and the height information in z axis. Then, the grid method is exploited for the environment modeling problem. After that, a recently proposed switching local evolutionary PSO (SLEPSO) based on non-homogeneous Markov chain and DE is analyzed for the path planning problem. The velocity updating equation of the presented SLEPSO algorithm jumps from one mode to another based on the non-homogeneous Markov chain, which can overcome the contradiction between local and global search. In addition, DE mutation and crossover operations can enhance the capability of finding a better global best particle in the PSO method. Findings Finally, the SLEPSO algorithm is successfully applied to the path planning in two different environments. Comparing with some well-known PSO algorithms, the experiment results show the feasibility and effectiveness of the presented method. Originality/value Therefore, this can provide a new method for the area of path planning of intelligent robot.

Dynamic topology multi force particle swarm optimization algorithm and its application

Particle swarm optimization (PSO) algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization (DTMFPSO) algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization (MFPSO) algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing (FE) topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as µPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.

Non-parametric particle swarm optimization for global optimization

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.

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

In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.

A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay

This paper presents a novel particle swarm optimization (PSO) based on a non-homogeneous Markov chain and differential evolution (DE) for quantification analysis of the lateral flow immunoassay (LFIA), which represents the first attempt to estimate the concentration of target analyte based on the well-established state-space model. A new switching local evolutionary PSO (SLEPSO) is developed and analyzed. The velocity updating equation jumps from one mode to another based on the non-homogeneous Markov chain, where the probability transition matrix is updated by calculating the diversity and current optimal solution. Furthermore, DE mutation and crossover operations are implemented to improve local best particles searching in PSO. Compared with some well-known PSO algorithms, the experiments results show the superiority of proposed SLEPSO. Finally, the new SLEPSO is successfully exploited to quantification analysis of the LFIA system, which is essentially nonlinear and dynamic. Therefore, this can provide a new method for the area of quantitative interpretation of LFIA system.

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 Newton’s 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.

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.

Partitioned Particle Swarm Optimization

The particle swarm optimization (PSO) is a population-based optimization method inspired by flocking behavior of birds and human social interactions. So far, numerous modifications of PSO algorithm have been published, which make the PSO method more complex. Several improved PSO versions succeed in keeping the diversity of the particles during the searching process, but at the expense of convergence speed. This paper is aimed at increasing the rate of convergence and diversity of solutions in the population via two easy techniques: (a) Applying improved acceleration coefficients (b) Dividing search space into blocks. In particular, the second technique is efficient in the case of functions with optimal design variables situated in the one block. Hence, instead of proposing more complex variant of PSO, a simplified novel technique, called Partitioned Particle Swarm Optimizer (PPSO), has been proposed. In order to find optimal coefficients of this method, an extensive set of experiments were conducted. Experimental results and analysis demonstrate that PPSO outperforms nine well-known particle swarm optimization algorithms with regard to global search.

A particle swarm optimizer for grouping problems

Many combinatorial optimization problems comprise a grouping phase (the grouping problem) in which the task is to partition a set of items into disjoint sets. Introduced in 1994, grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we adapt the structure of the well-known particle swarm optimization algorithm (PSO) for grouping problems. To propose the grouping version of the PSO algorithm, which is called GPSO algorithm, we develop new particle position and velocity updating equations which preserve the major characteristics of the original equations and are respondent to the structure of grouping problems. The new updating equations work with groups of items rather than items isolatedly. One of the main characteristics of the new equations is that they work in continuous space but their outcome is used in discrete space through a two phase procedure. Applications of GPSO algorithm are made to the single batch–machine scheduling problem and bin packing problem, and results are compared with the results reported by GGA. Computational results testify that our algorithm is efficient and can be regarded as a new solver for the wide class of grouping problems.