Self-adaptive Particle Swarm Optimization Research Articles

Soft Actor-Critic Approach to Self-Adaptive Particle Swarm Optimisation

Particle swarm optimisation (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. The decentralised optimisation methodology of PSO is ideally suited to problems with multiple local minima and deceptive fitness landscapes, where traditional gradient-based algorithms fail. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. CPs that ensure good performance are problem-dependent. Unfortunately, CPs tuning is computationally expensive and inefficient. Self-adaptive particle swarm optimisation (SAPSO) algorithms aim to adaptively adjust CPs during the optimisation process to improve performance, ideally while reducing the number of performance-sensitive parameters. This paper proposes a reinforcement learning (RL) approach to SAPSO by utilising a velocity-clamped soft actor-critic (SAC) that autonomously adapts the PSO CPs. The proposed SAC-SAPSO obtains a 50% to 80% improvement in solution quality compared to various baselines, has either one or zero runtime parameters, is time-invariant, and does not result in divergent particles.

Improving Collaborative Recommender Systems by Integrating Fuzzy C-Ordered Means Clustering and Chaotic Self-Adaptive Particle Swarm Optimization Algorithm

Improving Collaborative Recommender Systems by Integrating Fuzzy C-Ordered Means Clustering and Chaotic Self-Adaptive Particle Swarm Optimization Algorithm

Multi-Objective Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification.

Feature selection (FS) is recognized for its role in enhancing the performance of learning algorithms, especially for high-dimensional datasets. In recent times, FS has been framed as a multi-objective optimization problem, leading to the application of various multi-objective evolutionary algorithms (MOEAs) to address it. However, the solution space expands exponentially with the dataset's dimensionality. Simultaneously, the extensive search space often results in numerous local optimal solutions due to a large proportion of unrelated and redundant features [H.Adeli and H.S. Park, Fully automated design of super-high-rise building structures by a hybrid ai model on a massively parallel machine, AI Mag.17 (1996) 87-93]. Consequently, existing MOEAs struggle with local optima stagnation, particularly in large-scale multi-objective FS problems (LSMOFSPs). Different LSMOFSPs generally exhibit unique characteristics, yet most existing MOEAs rely on a single candidate solution generation strategy (CSGS), which may be less efficient for diverse LSMOFSPs [H.S. Park and H.Adeli, Distributed neural dynamics algorithms for optimization of large steel structures, J. Struct. Eng. ASCE123 (1997) 880-888; M.Aldwaik and H.Adeli, Advances in optimization of highrise building structures, Struct. Multidiscip. Optim.50 (2014) 899-919; E.G. González, J.R. Villar, Q.Tan, J.Sedano and C.Chira, An efficient multi-robot path planning solution using a* and coevolutionary algorithms, Integr. Comput. Aided Eng.30 (2022) 41-52]. Moreover, selecting an appropriate MOEA and determining its corresponding parameter values for a specified LSMOFSP is time-consuming. To address these challenges, a multi-objective self-adaptive particle swarm optimization (MOSaPSO) algorithm is proposed, combined with a rapid nondominated sorting approach. MOSaPSO employs a self-adaptive mechanism, along with five modified efficient CSGSs, to generate new solutions. Experiments were conducted on ten datasets, and the results demonstrate that the number of features is effectively reduced by MOSaPSO while lowering the classification error rate. Furthermore, superior performance is observed in comparison to its counterparts on both the training and test sets, with advantages becoming increasingly evident as the dimensionality increases.

Belief space-guided approach to self-adaptive particle swarm optimization

Particle swarm optimization (PSO) performance is sensitive to the control parameter values used, but tuning of control parameters for the problem at hand is computationally expensive. Self-adaptive particle swarm optimization (SAPSO) algorithms attempt to adjust control parameters during the optimization process, ideally without introducing additional control parameters to which the performance is sensitive. This paper proposes a belief space (BS) approach, borrowed from cultural algorithms (CAs), towards development of a SAPSO. The resulting BS-SAPSO utilizes a belief space to direct the search for optimal control parameter values by excluding non-promising configurations from the control parameter space. The resulting BS-SAPSO achieves an improvement in performance of 3–55% above the various baselines, based on the solution quality of the objective function values achieved on the functions tested.

Optimal DoS attack on multi-channel cyber-physical systems: A Stackelberg game analysis

In this commentary, optimal denial-of-service (DoS) attack strategies on multi-channel cyber-physical systems (CPSs) are considered, which focus on energy allocation on communication channels. For simplicity, a Stackelberg game between one defender and one attacker is constructed. Compared with the existing literature, which mainly pay attention to static equilibrium, the dynamic process of the game is also exhibited in this paper, which fills the gap in the demonstration of dynamic decision-making between both players of the game. In the solution of Stackelberg equilibrium, a self-adaptive particle swarm optimization (PSO) algorithm with Sigmoid-like update function is applied to cope with the nonlinearity of the reward function with faster convergence and wider adaptability. Besides, to acquire better performance of both sides to allocate energy, an online computation algorithm is proposed for dynamic Stackelberg game. Finally, numerical examples are provided to illustrate similarities between theoretic static equilibrium and optimal strategies obtained by Monte Carlo simulations.

Optimal DoS attack strategy for cyber-physical systems: A Stackelberg game-theoretical approach

In this paper, the optimal DoS attack on cyber-physical systems is studied in Stackelberg game framework, which is manifested by energy allocation on communication channels. To give a realistic picture of DoS attack on a normal user launched by several hackers, a Stackelberg game between one defender and multiple attackers is considered. Compared with the existing literature, which mainly focus on equilibrium in static games, the dynamic process of the Stackelberg game is also reflected in this paper, which demonstrates the intelligence of attackers switching channels to allocate aggressive energy. In the solution of Stackelberg equilibrium, self-adaptive Particle Swarm Optimization (PSO) algorithm is utilized to cope with the nonlinearity of the reward function. Besides, for the purpose of better performance of both sides in choosing channels to allocate energy, an online computation algorithm is proposed. Finally, numerical examples are equipped to illustrate the theoretical results proved in this work.

An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase

Over the past two decades, to overcome the limitations of certain algorithms, ensemble strategies or self-adaptive mechanisms for evolutionary computation algorithms have been proposed. Regardless of how these strategies or mechanisms were designed, their objective was to control the balance between the global and local search capabilities during the evolutionary process. Inspired by this, a novel ensemble strategy with three groups to improve the performance of the ensemble particle swarm optimizer (EPSO) is proposed. The first group uses a covariance matrix adapted retreat phase (CMAR), the second group induces the niching behavior for inertia weight particle swarm optimization (PSO), and the third group maintains the characteristics of a large subpopulation of EPSO. Furthermore, a sample pool and replacement mechanism are proposed to perturb the three groups. This strategy also recommends a group of empirical allocation rates for subpopulations based on various proportion combination tests. The performance of the proposed strategy is evaluated using CEC2005 benchmark functions with 10, 30, and 50-dimensional tests and compared with those of the state-of-the-art PSO variants: EPSO, a modified PSO using adaptive strategy (MPSO), PSO variant for single-objective numerical optimization (PSO-sono), terminal crossover and steering-based PSO (TCSPSO), self-adapting hybrid strategy PSO (SaHSPS), and pyramid PSO (PPSO). Experimental results demonstrate that the improved EPSO, using CMAR, niching behavior, and sample pool, performs best.

A Novel Feedforward Model of Piezoelectric Actuator for Precision Rapid Cutting.

The piezoelectric actuator has been widely used in modern precision cutting technology due to its fast response speed and high positioning accuracy. In recent years, with the development of precision technology, modern cutting requires higher and higher cutting accuracy and efficiency. Therefore, this paper proposes a feedforward control method based on the modified Bouc-Wen (MBW) model. Firstly, a novel asymmetrical modified Bouc-Wen model with an innovative form of shape control function is developed to model the hysteresis nonlinearity property of piezoelectric actuators. Then, a self-adaptive cooperative particle swarm optimization (PSO) algorithm is developed to identify the parameters of MBW model. The comparative evaluation reveals that the MBW model outperforms the classical Bouc-Wen (CBW) model by 66.4% in modeling accuracy. Compared with traditional PSO algorithm, the self-adaptive cooperative PSO algorithm can obtain minimum fitness in parameter identification. Furthermore, the feedforward control strategy is realized to improve the position tracking accuracy. A position tracking experiment verifies that the feedforward control strategy improves the tracking accuracy of piezoelectric actuators significantly compared with the open-loop control strategy.

Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles

The energy crises and environmental problem can be solved by using plug-in hybrid electric vehicles (PHEVs), the integration of large number of PHEVs with high control capabilities and storage can improve the distribution network flexibility. However, the optimal management of these vehicles in the presence of renewable energy resources (RESs) represents a big challenge that must be adopted, this can be accomplished in the form of microgrid (MG). Therefore, this paper proposes a new energy management strategy (EMS) incorporated bald eagle search (BES) optimizer for MG with RESs and PHEVs to regulate the generation of each unit. The considered devices installed in the MG are wind turbine (WT), photovoltaic (PV), micro turbine (MT), fuel cell (FC), storage battery, PHEVs, and grid. The problem is formulated as an optimization problem that aims at minimizing the total operating cost and mitigating the environmental pollutant emission. Two scenarios related to the operation of RESs are considered in addition to three charging modes of EVs which are uncoordinated, coordinated, and smart. The proposed BES-EMS is validated via conducting comparison to literature works of Fuzzy self-adaptive particle swarm optimizer (FSAPSO) and gravitational search and pattern search (GSA-PS) algorithm in addition to new programmed optimizers of Runge Kutta optimization (RUN), mountain gazelle optimizer (MGO), chef-based optimization algorithm (CBOA), beluga whale optimization (BWO), and dandelion optimizer (DO). Moreover, statistical tests of Friedman rank, ANOVA table, Wilcoxon rank, and Kruskal Wallis are conducted to assess the proposed BES-EMS. In scenario (1), the proposed BES outperformed all optimizers achieving the best operating cost and emission of 111.2518 €ct and 182.0741 kg respectively, the cost is saved by 57.66 % compared to GSA-PS while the emission is mitigated by 56.86 % compared to FSAPSO. In scenario (2), the proposed BES-EMS mitigated the cost and emission by 70.68 % and 51.78 % rather than GSA-PS and FSAPSO respectively. Moreover, in scenrio (3) the proposed approache saved the cost by 61.49 % and 67.29 % compared to GSA-PS during smart charging of PHEVs in normal and rated operations of RESs respectively. Furthermore, according to the Friedman rank test the proposed BES-EMS achieved the first rank with p-value of 2.1. The fetched results proved the robustness and competence of the proposed BES as an efficient energy management strategy for MG.

An efficient honey badger algorithm for scheduling the microgrid energy management

Nowadays, it is essential for modern grids to operate at optimal scheduling, this helps in reducing the energy costs, mitigating the pollutant emissions, and making better use of renewable energy resources (RESs) such as photovoltaic (PV) and wind turbine (WT). Therefore, this paper proposes an energy management scheme for microgrid (MG) using recent metaheuristic honey badger algorithm (HBA) to enhance its operation via identifying the optimal scheduling of the installed generation units. HBA has the ability to solve complex optimization problem and avoid stuck in local optima due to balance between the exploration and exploitation phases. The constructed MG composes PV, WT, microturbine (MT), fuel cell (FC), and battery storage system. Operation of PV and WT at their normal generations, operation of WT at its rated power, and operation of PV and WT at their maximum limits are three cases analyzed in this work. Two objective functions are considered which are mitigating the operating cost and minimizing the pollutant emission. The proposed HBA is evaluated via conducting comparison to some reported approaches like Fuzzy self-adaptive particle swarm optimizer (FSAPSO), sparrow search algorithm (SSA), and gravitational search and pattern search algorithm (GSA-PS). Moreover, other programmed approaches of aquila optimizer (AO), Tasmanian devil optimizer (TDO), artificial rabbits optimizer (ARO), coronavirus herd immunity optimizer (CHIO), manta-ray foraging optimizer (MRFO), and dynamic arithmetic optimization approach (DAOA) are implemented and compared to the proposed algorithm. The fetched results proved the robustness and preference of HBA in achieving the best operation of MG in all studied operating conditions.

Self-Adapting Particle Swarm Optimization for continuous black box optimization

This paper introduces a new version of a hyper-heuristic framework: Generalized Self-Adapting Particle Swarm Optimization with samples archive (M-GAPSO). This framework is based on the authors previous works on hybridization of optimization algorithms and enhancing population based optimization with model based optimization. The paper presents the structure of the proposed framework and analyzes the impact of its modules on the final system performance. M-GAPSO hybridizes Particle Swarm Optimization, Differential Evolution and model based optimizers. A ratio of particular algorithms within a population is regulated by an adaptation scheme. The applicability of the proposed hybrid method to black-box optimization is verified on 24 continuous benchmark functions from the COCO test set and 29 functions from the CEC-2017 test set. On the BBOB test set a hybrid of PSO and DE with adaptation obtained 11 significantly better and 2 significantly worse results on 5 and 20 dimensional functions than the basic DE. Further inclusion of the model based optimizers led to 15 significantly better and 2 significantly worse results compared to the PSO-DE hybrid. On the CEC-2017 test set, M-GAPSO was significantly better than both Red Fox Optimization and Dual Opposition-Based Learning for Differential Evolution (DOBL) on 7 functions in 30 dimensions and 12 functions in 50 dimensions.

Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

Low economic risk operation of transactive energy markets with renewable sources and virtual power plants using self-adaptive particle swarm optimization

Low economic risk operation of transactive energy markets with renewable sources and virtual power plants using self-adaptive particle swarm optimization

The cold rolling load distribution of the nuclear power zirconium alloy based on the self-adaptive particle swarm optimization algorithm

Aiming at the problem of load distribution during multi-pass cold rolling of nuclear zirconium alloy strip, the load distribution model with good plate shape is established by the self-adaptive particle swarm optimization (SAPSO) algorithm, considering the main constraint conditions including rolling force, reduction, and torque in cold rolling process. Based on the penalty function method transforming the constraint problem into the unconstrained problem, the particle swarm optimization algorithm (PSO) combined with self-adaptive inertia weight factor optimized the load distribution model is developed to improve the local search ability of the particle swarm optimization algorithm. Compared with the original nuclear zirconium alloy cold rolling schedule, the simulation results of load distribution based on the SAPSO algorithm can keep good plate shape in multi-pass cold rolling process with the high prediction accuracy. The industrial experiments demonstrate that the proportional crown difference value is consistent, and the plate shape flatness is good.

Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm

The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs is an ideal method to solve this problem. In this study, a day-ahead optimal scheduling model for IES that included an electrical system, a natural gas system, and an energy hub (EH), was established. The proposed EH contained detailed models of the fuel cell (FC) and power to gas (P2G) system. Considering that the optimal scheduling of an IES is a non-convex complex optimal problem, a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on multistage chaotic mapping was proposed to solve it. The objective was to minimize the operating cost of the IES. Three operation scenarios were designed to analyze the operation characteristics of the system under different coupling conditions. The simulation results showed that the PCAPSO algorithm improved the convergence rate and stability compared to the original PSO. An analysis of the results demonstrated the economics of an IES with the proposed EHs and the advantage of cooperation between the FC and P2G system.

Design of electronic cam for lower hook mechanism of fishing net-weaving machine based on polynomial fitting

The working efficiency and stability of the double hook-based fishing net-weaving machine is mainly determined by the lower hook mechanism. In this work, a new kind of lower hook mechanism, which is driven by four servo motors, is presented, and the electronic cam curve of the lower hook mechanism is introduced. First, cubic B-spline interpolation is used to get the basic motion path of the lower hook plate, and then the piecewise quintic polynomial fitting method is used to fit the motion path. Finally, self-adaptive mutation-based particle swarm optimization is put forward and used to obtain the optimal parameters of the quintic polynomial, which performs better compared with the other two particle swarm optimization algorithms in this study. Experiments suggest that the electronic cam curve generated by the piecewise quintic polynomial fitting has got 55.91% (horizontal motors) and 60.96% (vertical motors) optimization in maximum motor torque compared with curves generated by cubic B-spline interpolations. In addition, the new lower hook mechanism and its moving curve described in this paper improved the theoretical weaving speed of the fishing net-weaving machine, providing a basis for digital improvement of the knotted net-weaving industry.

Self-Adaptive Multi-Task Differential Evolution Optimization: With Case Studies in Weapon–Target Assignment Problem

Multi-task optimization (MTO) is related to the problem of simultaneous optimization of multiple optimization problems, for the purpose of solving these problems better in terms of optimization accuracy or time cost. To handle MTO problems, there emerges many evolutionary MTO (EMTO) algorithms, which possess distinguished strategies or frameworks in the aspect of handling the knowledge transfer between different optimization problems (tasks). In this paper, we explore the possibility of developing a more efficient EMTO solver based on differential evolution by introducing the strategies of a self-adaptive multi-task particle swarm optimization (SaMTPSO) algorithm, and by developing a new knowledge incorporation strategy. Then, we try to apply the proposed algorithm to solve the weapon–target assignment problem, which has never been explored in the field of EMTO before. Experiments were conducted on a popular MTO test benchmark and a WTA-MTO test set. Experimental results show that knowledge transfer in the proposed algorithm is effective and efficient, and EMTO is promising in solving WTA problems.

State of Health Estimation Method for Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network Model With Exogenous Input

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this article examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian information criterion (BIC). Based on the correlation between capacity and resistance, this article concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multibattery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive Particle Swarm Optimization Differential Evolution algorithm to estimate state of charge

State of charge (SOC) estimation is a significant task for lithium-ion batteries. However, the accuracy of SOC estimation is closely related to parameters of battery and system non-linearity. To identify the parameters of lithium-ion battery better, this proposed a self-adaptive particle swarm optimization differential evolution (SaPSODE) algorithm. First, to describe the dynamic behaviors of battery, we presented a first-order RC equivalent circuit model (ECM). Second, to calculate open-circuit voltage (OCV) versus time during the dynamic test procedure, an optimizing objective function was built to minimize errors between the true and optimized terminal voltages. Further, control parameters of F and Cr were also self-adapted according to previous successful records. Third, by using OCV-SOC mapping curves, this work obtained estimated SOC curve which was compared with true SOC curve. Comprehensive experimental results demonstrated effectiveness of the proposed framework and methodology, compared with several highly-cited DE variants.

Self-adaptive Particle Swarm Optimization with Human-in-the-loop for Ankle Exoskeleton Control

Self-adaptive Particle Swarm Optimization with Human-in-the-loop for Ankle Exoskeleton Control