Canonical PSO Research Articles

Automated Design of Fault Diagnosis CNN Network for Satellite Attitude Control Systems.

Despite the dominance of unsupervised and self-supervised anomaly detection methods in the current satellite fault diagnosis domain, supervised anomaly detection offers a superior alternative for high-sensitivity detection and lightweight deployment requirements specific to subsystems or components, such as attitude control systems (ACSs). This article addresses the issues of over-design and insufficient accuracy in the CNN network design for satellite ACS fault diagnosis by introducing the modified particle swarm optimization-advanced convolution blocks-based CNN (MPSO-ACBCNN) method. First, we present the ACBCNN, a lightweight, flexible-layer CNN architecture. This architecture leverages advanced convolution blocks (ACBs), which incorporate numerous efficient design elements to enhance feature extraction capabilities within power spectral density (PSD) graphs of various fault samples, and employs classical dense connection methods to prevent the issue of gradient vanishing. Second, we devise the MPSO-ACBCNN algorithm to optimize the ACBCNN fault diagnosis architecture for specified ACS using MPSO. In MPSO-ACBCNN, several optimizations to the canonical PSO are implemented, including the fitness design that balances the tradeoff between total parameter quantity and the training effectiveness, and methods to ensure feasible solutions, etc. Finally, numerical experimental results demonstrate the effectiveness and superiority of MPSO-ACBCNN in fault diagnosis for ACS.

Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Scientific workflow scheduling is a well-known problem that involves the allocation of workflow tasks to particular computational resources. The generation of optimal solutions to reduce run-time, cost, and energy consumption, as well as ensuring proper load balancing, remains a major challenge. Therefore, this work presents a Multi-Swarm based Particle Swarm Optimization (MS-PSO) algorithm to improve the scheduling of scientific workflows in cloud-fog environments. In particular, MS-PSO seeks to address the canonical PSO’s problem of premature convergence, which leads it to suboptimal solutions. In MS-PSO, particles are divided into several swarms, with each swarm having its own cognitive and social learning coefficients. This work also develops a weighted sum objective function for the workflow scheduling problem, based on four objectives: makespan, cost, energy and load balancing for cloud and fog tiers. The FogWorkflowSim Toolkit is used in the evaluation process, with the objectives serving performance metrics. The MS-PSO approach is compared with the canonical PSO, Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. The following scientific workflows are used in the simulations: Montage, Cybershake, Epigenomics, LIGO and SIPHT. MS-PSO outperforms the canonical PSO on all scientific workflows and under all performance metrics. It competes fairly well against the other approaches and it is more stable and reliable. It only ranks second to PSO, in terms of execution time. In future, multiple species, incorporating population update mechanisms from several algorithmic frameworks (MS-PSO, DE, GA), will be used for scientific workflow scheduling. Hybdridization of the realized algorithm with dynamic approaches will also be investigated.

Diversity collaboratively guided random drift particle swarm optimization

The random drift particle swarm optimization (RDPSO) algorithm is an effective random search technique inspired by the trajectory analysis of the canonical PSO and the free electron model in metal conductors placed in an external electric field. However, like other PSO variants, the RDPSO algorithm also inevitably encounters premature convergence when solving multimodal problems. To address this issue, this paper proposes a novel diversity collaboratively guided (DCG) strategy for the RDPSO algorithm that enhances the search ability of the algorithm. In this strategy, two kinds of diversity measures are defined and modified in a collaborative manner. Specifically, the whole search process of the RDPSO is divided into three phases based on the changes in the two diversity measures. In each phase, different values are selected for the key parameters of the update equation in the RDPSO to make the particle swarm perform different search modes. Consequently, the improved RDPSO algorithm with the DCG strategy (DCG-RDPSO) can maintain its diversity dynamically at a certain level, and thus can search constantly without stagnation until the search process terminates. The performance evaluation of the proposed algorithm is done on the CEC-2013 benchmark suite, in comparison with several versions of RDPSO, different variants of PSO and several non-PSO evolutionary algorithms. Experimental results show that the proposed DCG strategy can significantly improve the performance and robustness of the RDPSO algorithm for most of the multimodal problems. Further experiments on economic dispatch problems also verify the effectiveness of the DCG strategy.

Node placement optimization using particle swarm optimization and iterated local search algorithm in wireless sensor networks

SummaryCoverage and connectivity are the two most challenging issues in target‐based wireless sensor networks (WSNs). For that, node placement is one of the fundamental concerns that affect the performance of coverage and connectivity in WSN. This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO‐ILS) to have an optimum coverage and connectivity rate with the minimum number of nodes. In one side, to maintain the full coverage of targets, the PSO‐ILS is used to deploy the minimum number of sensor nodes. In other side, to achieve the full connectivity, the optimal position determination (OPD) algorithm was conceived to identify the optimal candidate positions which can be used by the PSO‐ILS to place the minimum number of relay nodes. The obtained results considered over a number of runs are compared with canonical PSO, differential evolution (DE), and genetic algorithms (GAs). The outcomes derived from this comprehensive analysis determine that PSO‐ILS provides an effectual improvement in contrary to the methods PSO, DE, and GA in terms of the selected potential positions to ensure full coverage of target points and the number of relay nodes required to achieve full connectivity.

Multi-robot path planning in wireless sensor networks based on jump mechanism PSO and safety gap obstacle avoidance

In order to meet the real-time and accurate requirements of multi-robot path planning in dynamic environment, this paper adopted wireless sensor network to locate robots and obstacles and used an improved artificial intelligent algorithm to plan path. In this paper, a jumping mechanism particle swarm optimization (JPSO) algorithm and a safety gap obstacle avoidance algorithm (SGOA) algorithm were proposed. Compared with canonical PSO algorithm, JPSO algorithm has three improvement strategies: Fitness value evaluation function, new learning sample and jumping strategy. The JPSO algorithm updates the particles with poor comprehensive quality by jumping and adjusts the inertia weight adaptively according to fitness value evaluation function. With the cooperation of new learning samples, the global searching ability and precision of the algorithm can be improved. SGOA algorithm is mainly aimed at the problem that robots with low priority are stuck in a long wait and cannot continue to walk when avoiding obstacles. By implementing the SGOA algorithm, a new collision-free safety path can be optimized for the robot with low priority. In order to verify JPSO and SGOA algorithm, a lot of experiments were done. JPSO algorithm was compared with two other improved PSO algorithms with 6 standard test functions. The path planning and obstacle avoidance experiments of six robots were realized using the JPSO and SGOA algorithm. The experimental results show that JPSO algorithm has higher accuracy and faster convergence speed than the other two improved PSO algorithms, and SGOA algorithm can solve the dynamic obstacle avoidance problem in the path planning of multiple robots well.

Robot global path planning using PSO algorithm based on the interaction mechanism between leaders and individuals

To overcome the disadvantages of low optimization accuracy and prematurity of the canonical PSO algorithm, we proposed an improved particle swarm optimization based on the interaction mechanism between leaders and individuals (PSO-IBLI), and used it to implement robot global path planning. In the PSO-IBLI algorithm, in different stages, each particle learns from the elites according to different regular. Moreover, the improved algorithm divides the execution state into two categories, where the parameters and the evaluation mechanisms are varied accordingly. In this way, the global best particles no longer walk randomly and have more learning objects. At the same time, other particles learn from not only the global best position, their historical best positions, but also the other elites. The learning strategy makes the search mode always in the adaptive adjustment, and it improves the speed of convergence and promotes this algorithm to find a more precise solution. The experimental results suggest that the precision and convergence speed of the PSO-IBLI algorithm is higher than the other three different algorithms. Additionally, some experiments are carried out to plan the robot’s entire collision-free path using the PSO-IBLI algorithm and the other three algorithms. The results show that the PSO-IBLI algorithm can obtain the shortest collision-free way in four algorithms.

A Novel Multi-Epoch Particle Swarm Optimization Technique

Abstract Since canonical PSO method has many disadvantages which do not allow to effectively reach the global minima of various functions it needs to be improved. The article refers to a novel Multi-Epoch Particle Swarm Optimization (ME-PSO) technique which has been developed by authors. ME-PSO algorithm is based on reinitializing of the stagnant swarm with low exploration efficiency. This approach provides a high rate of global best changing. As a result ME-PSO has great possibility of finding good local (or even global) optimum and does not trap in bad local optimum. In order to prove the advantages of the ME-PSO technique numerical experiments have been carried out with ten uni- and multimodal benchmark functions. Analysis of the obtained results convincingly showed significant superiority of ME-PSO over PSO and IA-PSO algorithms. It has been set that canonical PSO is a special case of ME-PSO.

Hybrid teaching–learning‐based PSO for trajectory optimisation

A hybrid modified teaching–learning-based particle swarm optimisation (HMTL-PSO) initialised by the normalised step cost (NSC), named HMTL-NSCPSO, is proposed for solving trajectory optimisation with complex constraint problems. Specially, the new HMTL-NSCPSO combines the canonical PSO basic policy, the teaching–learning-based optimisation (TLBO) algorithm and the normalised step cost (NSC) function in order to promote diversity, obtain well-speed convergence and to improve search ability. The algorithm is tested on an UAV trajectory optimization problems. Experimental results validate the effectiveness of the HMTL-NSCPSO.

A novel hybrid teaching learning based multi-objective particle swarm optimization

How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to promote the diversity and improve search ability. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence.

Chaos Enhanced Differential Evolution in the Task of Evolutionary Control of Discrete Chaotic LOZI Map

In this paper, evolutionary technique Differential Evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of selected discrete chaotic system, which is the two-dimensional Lozi map. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used within Chaos enhanced heuristic concept as the chaotic pseudo-random number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudo-random sequences given by chaotic map to help Differential evolution algorithm in searching for the best controller settings for the same chaotic system. The optimizations were performed for three different required final behavior of the chaotic system, and two types of developed cost function. To confirm the robustness of presented approach, comparisons with canonical DE strategy and PSO algorithm have been performed.

Monkey King Evolution: A new memetic evolutionary algorithm and its application in vehicle fuel consumption optimization

Optimization algorithms are proposed to tackle different complex problems in different areas. In this paper, we firstly put forward a new memetic evolutionary algorithm, named Monkey King Evolutionary (MKE) Algorithm, for global optimization. Then we make a deep analysis of three update schemes for the proposed algorithm. Finally we give an application of this algorithm to solve least gasoline consumption optimization (find the least gasoline consumption path) for vehicle navigation. Although there are many simple and applicable optimization algorithms, such as particle swarm optimization variants (including the canonical PSO, Inertia Weighted PSO, Constriction Coefficients PSO, Fully Informed Particle Swarm, Comprehensive Learning Particle Swarm Optimization, Dynamic Neighborhood Learning Particle Swarm). These algorithms are less powerful than the proposed algorithm in this paper. 28 benchmark functions from BBOB2009 and CEC2013 are used for the validation of robustness and accuracy. Comparison results show that our algorithm outperforms particle swarm optimizer variants not only on robustness and optimization accuracy, but also on convergence speed. Benchmark functions of CEC2008 for large scale optimization are also used to test the large scale optimization characteristic of the proposed algorithm, and it also outperforms others. Finally, we use this algorithm to find the least gasoline consumption path in vehicle navigation, and conducted experiments show that the proposed algorithm outperforms A* algorithm and Dijkstra algorithm as well.

Canonical PSO Based K-Means Clustering Approach for Real Datasets.

“Clustering” the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System

In this investigation, a novel fuzzy mathematical program based on thermodynamic principles is implemented to capture the uncertainties of a practical power system, known as Damavand power plant. The proposed intelligent machine takes the advantages of a niching bio-inspired learning mechanism to be reconciled to the requirements of the problem at hand. The aim of the bio-inspired fuzzy based intelligent system is to yield a model capable of recognizing different operating parameters of Damavand power system under different operating conditions. To justify the privileges of using a niching metaheuristic over gradient descend methods, the authors use the data, derived through data acquisition, together with a machine learning based approach to estimate the multi-modality associated with the training of the proposed fuzzy model. Moreover, the niching bio-inspired metaheuristic, niching particle swarm optimization (NPSO), is compared to canonical PSO (CPSO), stochastic social PSO (SSPSO), unified PSO (UPSO), comprehensive learning PSO (CLPSO), PSO with constriction factor (PSOCF) and fully informed PSO (FIPSO). Through experiments and analysis of the characteristics of the problem being optimized, it is proved that NPSO is not only able to tackle the deficiencies of the learning process, but also can effectively adjust the fuzzy approach to conduct the identification process with a high degree of robustness and accuracy.

APPLICATION OF PARTICLE SWARM OPTIMIZATION FOR ENHANCED CYCLIC STEAM STIMULATION IN A OFFSHORE HEAVY OIL RESERVOIR

Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and L\'evy Bare-bone PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of these algorithms was compared in terms of convergence behaviour and the final optimization results. It is found that, in general, all three types of PSO methods are able to improve the objective function. The best objective function is found by using the Canonical PSO, while the other two methods give similar results. The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The L\'evy Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization problem investigated in this study, it is found that the temperature of the injection steam, CO2 composition in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam injection rate and the liquid production rate have less impact on the objective function.

Application of Particle Swarm Optimization for Enhanced Cyclic Steam Stimulation in a Offshore Heavy Oil Reservoir

Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and Levy Bare-bone PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of these algorithms was compared in terms of convergence behaviour and the final optimization results. It is found that, in general, all three types of PSO methods are able toimprove the objective function. The best objective function is found by using the Canonical PSO, while the other two methods give similar results. The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The Levy Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization problem investigatedin this study, it is found that the temperature of the injection sCO2 composition in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam injection rate and the liquid production rate ha ve less impact on the objective function.

A Canonical Complementary PSO Approach in Radial Distribution System for Reduction of Total Line Loss and Improvement of Voltage Profile

A Canonical Complementary PSO Approach in Radial Distribution System for Reduction of Total Line Loss and Improvement of Voltage Profile

An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.

Application of evolutionary optimization techniques for finding the optimal set of concentric circular antenna array

In this paper the maximum sidelobe level (SLL) reductions, optimal beam patterns and optimal beam widths of various designs of three-ring planar concentric circular antenna arrays (PCCAA) are examined using three different classes of evolutionary optimization techniques to finally determine the global optimal three-ring PCCAA design and then establish some sort of ranking among the techniques. Apart from physical construction of a PCCAA, one may broadly classify its design into two major categories: uniformly excited arrays and non-uniformly excited arrays. The present paper assumes non-uniform excitations and uniform spacing of excitation elements in each three-ring PCCAA design and a design goal of maximizing SLL reduction associated with optimal beam patterns and beam widths. The design problem is modeled as an optimization problem for each PCCAA design and solved using different evolutionary optimization techniques to determine an optimum set of normalized excitation weights for PCCAA elements, which, when incorporated, results in a radiation pattern with optimal (maximum) SLL reduction. Among the various PCCAA designs, one which yields the global minimum SLL with global minimum first null beamwidth is the global optimal design. In this work the three-ring PCCAA containing ( N 1 = 4, N 2 = 6, N 3 = 8) elements proves to be such global optimal design. The optimization techniques employed are real coded GA (RGA), canonical PSO (CPSO), craziness based PSO (CRPSO), evolutionary programming (BEP), hybrid evolutionary programming (HEP). While ranking the techniques after 30 total runs for each design, HEP, CRPSO, RGA, CPSO, BGA hold the first five ranks in order of optimization capability. HEP yields global minimum SLL (−32.86 dB) and global minimum BWFN (77.0°) for the optimal design. BEP often changes the rank from second to fifth depending on the design set. Further, when compared to a uniformly excited PCCAA having equal number of elements and same radii a reduction of major lobe beamwidth is also observed in the optimal non-uniformly excited case.

RFID network planning using a multi-swarm optimizer

In this paper, we develop an optimization model for planning the positions of readers in the RFID network based on a novel multi-swarm particle swarm optimizer called PS 2O. The main idea of PS 2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. This algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With five mathematical benchmark functions, PS 2O is proved to have significantly better performance than five successful variants of PSO. PS 2O is then used for solving the real-world RFID network planning problem. Simulation results show that the proposed algorithm proves to be superior for planning RFID networks than canonical PSO, multi-swarm cooperative PSO (MCPSO), and two evolutionary algorithms, namely genetic algorithm with elitism (EGA) and self-adaptive evolution strategies (SA-ES), in terms of optimization accuracy and computation robustness.

Optimization based on symbiotic multi-species coevolution

This paper presents a general optimization model gleaned ideas from the coevolution of symbiotic species in natural ecosystems. Species extinction and speciation events are also considered in this model to tie it closer to natural evolution, as well as improve the algorithm robustness. This model is instantiated as a novel multi-species optimizer, namely PS 2O, which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic coevolution between species. When tested against benchmark functions, the PS 2O markedly outperforms the canonical PSO algorithm in terms of accuracy, robustness and convergence speed.