Multi-objective Particle Swarm Research Articles

Low-velocity impact analysis and multi-objective optimization of hybrid carbon/basalt fibre reinforced composite laminate

Three-dimensional in-plane progressive damage model was developed for carbon/basalt fibre interlayer hybrid composites in this study, and cohesive element model was employed to simulate interlaminar damage. Numerical simulation of low-velocity progressive impact damage of hybrid composite laminate was carried out, and its accuracy was verified by physical impact test, with the maximum error of the maximum contact force, maximum displacement and energy absorption not exceeding 6.1% and the maximum error in damage area size within 4.8%. The effects of lay-up structure and angle on the impact properties of hybrid laminate were investigated through simulation. Finally, real number coding strategy was proposed for the parametric representation of the lay-up material and angle of each layer. Multi-objective optimization of hybrid laminate based on Kriging surrogate model and hybrid algorithm composed of elitist non-dominated sorting genetic (NSGA2) algorithm and multi-objective particle swarm (MPSO) algorithm was conducted. The optimal trade-off solution was selected from the Pareto solutions using technique for ordering preferences by similarity to ideal solution (TOPSIS) coupled with principal component analysis (PCA) methods. The coefficient of variance between peak force and maximum displacement of the optimized laminate was 49.3%, 44.9% and 31.4% lower than those of the pure CFRP laminate, pure BFRP laminate, and hybrid laminate before optimization, respectively, indicating that the balance degree of impact displacement and force has been substantially improved. The progressive impact damage simulation and parametric optimization design methodology proposed in this study provide useful guidance for the design of hybrid composite laminate.

Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images

With so many thyroid knobs (nodules) discovered by accident, it is critical to recognize as many aberrant knobs (nodules) as possible from fine-needle aspiration (FNA) biopsies or other medical procedures while excluding those that are virtually certainly benign. Thyroid ultrasonography, on the other hand, is prone to interobserver variability and subjective translations. An effective deep learning model for segmenting and categorizing thyroid nodules in this study follows the stages below: data collection from a well-known archive, The Thyroid Digital Image Database (TDID), which comprises ultrasound pictures from 298 patients, preprocessing using anisotropic diffusion filter (ADF) for removing noise and enhancing the images, segmentation using a bilateral filter for segmenting images, feature extraction using grey level occurrence matrix (GLCM), feature selection using Multi-objective Particle Swarm with Random Forest Optimization (MbPSRA) and finally classification happens were Residual U-Net will be used. Experiment evaluation states the proposed model outperforms well than other state-of-art models.

A multi-objective strategy for cost-effective microgrid solutions based on renewable energy sources

With the rapid urbanization and increasing energy demands, microgrids are recognizing renewable energy sources (RESs) as a valuable power generation option. However, efficiently managing the energy cost poses a significant challenge in integrating RESs with microgrids. To address this challenge, this study presents a novel approach utilizing a cost-effective multi-objective genetic algorithm (MOGA) to optimize power allocation among diverse generation units within the microgrid. The proposed MOGA algorithm aims to minimize generation costs by efficiently distributing the generated power from different sources in the microgrid vs the CO2 emission. By leveraging the genetic algorithm population, MOGA generates a diverse set of non-dominated solutions. Simulation results demonstrate the effectiveness of the proposed approach in reducing the cost of RESs in microgrids, surpassing the performance of other multi-objective optimization methods such as multi-objective particle swarm (MOPSO) and multi-objective wind-driven optimization (MOWDO).

Multi-objective optimization for a novel sandwich corrugated square tubes

The sandwich corrugated square tubes (SCSTs), which includes outer square tube (OST), inner square tube (IST) and middle corrugated square tube (CST), have been has been proved to have excellent energy absorption performances. In order to further increase its performance and obtain the optimal structural parameters, multi-objective optimization of SCSTs is carried out. The surrogate modals for the SCSTs were constructed, and the main effects and the interaction effects were analyzed in detail. Finally, multi-objective particle swarm (MOPSO) optimization was used for multi-objective optimization of the SCSTs. The optimization results show that Pareto frontier of the SCSTs can reach a larger range than the SCSTs with A = 0 mm. Due to the coupling effect, energy absorption (EA) and initial peak force (Fmax) of the optimal design solution of the SCSTs are increased by 13.76% and decreased by 8.76% respectively compared to the sum of IST, OST and CST crushing alone. Two key indicators, including Fmax and EA, have been improved at the same time.

Dual-polarized nanocrescent antenna designed using efficient optimization techniques.

A novel nanocrescent antenna with polarization diversity is introduced. It is formed from a crescent-shaped patch fed with a coupled strip transmission line. The antenna is located on top of a SiO2 thin film with a shielding ground layer underneath. The structure is supported by an arbitrary substrate. Polarization of the radiated field can be adjusted to be along either one of the two orthogonal polarizations based on which one of the two crescent patch modes is going to be excited. The excitation of either one of these two modes of the patch is achieved by switching between the two propagating modes of the feeding coupled strip transmission line. Using a dual-polarized antenna allows doubling the optical communication system's capacity via frequency reuse. The new crescent antenna dimensions are optimized to satisfy several goals, such as minimizing the losses, the deviation of the main beam direction away from broadside, and maximizing the radiation efficiency and axial ratio. Through the optimization process, simple surrogate kriging models replace the detailed electromagnetic simulation. The optimal response is achieved by applying two different optimizers. The first optimizer employs the design-centering technique using normed distances. The multiobjective particle swarm with the preference ranking organization method for enrichment evaluations is used by the second optimizer. In order to identify the critical dimensions to which the nanoantenna is most sensitive, a sensitivity analysis is used. The optimized antenna is capable of switching its radiation between two orthogonal pure linear polarizations with maximum radiation along the broadside direction. The size of the proposed antenna is about 500nm×500nm. Its impedance-matching bandwidth is higher than 30 THz centered around 193 THz (1550 nm). Its gain and radiation efficiency are higher than 5.2 dBi and 85%, respectively, all over the working frequency band.

REVIEW OF THE MULTI-OBJECTIVE SWARM INTELLIGENCE OPTIMIZATION ALGORITHMS

Multi-objective swarm intelligence (MOSI) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) that consists of two or more conflict objectives, in which improving an objective leads to the degradation of the other. The MOSI algorithms are based on the integration of single objective algorithms and multi-objective optimization (MOO) approach. The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. These reviewed algorithms were mainly developed to solve continuous MOPs. The review is based on how the algorithms deal with objective functions using MOO approaches, the benchmark MOPs used in the evaluation and performance metrics. Furthermore, it describes the advantages and disadvantages of each MOO approach and provides some possible future research directions in this area. The results show that several MOO approaches have not been used in most of the proposed MOSI algorithms. Integrating other different MOO approaches may help in developing more effective optimization algorithms, especially in solving complex MOPs. Furthermore, most of the MOSI algorithms have been evaluated using MOPs with two objectives, which clarifies open issues in this research area.

Cloud workflow scheduling algorithm based on multi-objective hybrid particle swarm optimisation

Cloud workflow scheduling algorithm based on multi-objective hybrid particle swarm optimisation

Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

An improved optimal allocation scheme of energy storage system in a distribution system based on transient stability

Along with the development of renewable energy generation technology, energy storage system (ESS), highly effective equipment for suppressing renewable energy fluctuations, plays an indispensable part in power system. A two-step method is proposed to optimally allocate ESS and droop controller considering ESS performances in time domain. In the first step, the top ranked solutions of EES size and locations are determined by economic analysis and Power Sensitivity Analysis (PSA). In the second step, using the solutions obtained in step one as the constraint of solution space, a new Multi-objective Particle Swarm Optimization-Non-dominated Sorting Genetic Algorithm Ⅲ (MOPSONSGA Ⅲ) based on Transient Stability Analysis (TSA) is proposed. Optimization objectives are ESS capital cost and the time-domain voltage and frequency performances subject to a sudden change of wind power. Compared with traditional optimal allocation schemes of ESS, the proposed method produces a economical ESS allocation scheme, while the fluctuation of frequency is cut by 57% and the times of the frequency beyond the limit is reduced by 30% in the IEEE-34 bus system.

A multi-objective optimization model for sustainable supply chain network with using genetic algorithm

Purpose The purpose of this paper is to design and optimize economic and environmental dimensions in a sustainable supply chain (SSC) network. This paper developed a mixed-integer linear programing (MILP) model to incorporate economical and environmental data for multi-objective optimization of the SSC network. Design/methodology/approach The overall objective of the present study is to use high-quality raw materials, at the same time the lowest amount of pollution emission and the highest profitability is achieved. The model in the problem is solved using two algorithms, namely, multi-objective genetic and multi-objective particle swarm. In this research, to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system. Findings The differences found between the genetic algorithms (GAs) and the MILP approaches can be explained by handling the constraints and their various logics. The solutions are contrasted with the original crisp model based on either MILP or GA, offering more robustness to the proposed approach. Practical implications The model is applied to Mega Motor company to optimize the sustainability performance of the supply chain i.e. economic (cost), social (time) and environmental (pollution of raw material). The research method has two approaches, namely, applied and mathematical modeling. Originality/value There is limited research designing and optimizing the SSC network. This study is among the first to integrate sustainable supplier selection and optimization of sustainability performance indicators in supply chain network design considering minimization of cost and time and maximization of sustainability indexes of the system.

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

An Improved Multi-Objective Quantum-Behaved Particle Swarm Optimization for Railway Freight Transportation Routing Design

With the development of railway transportation, the railway transportation enterprises expand their freight transportation from station-to-station transportation to door-to-door transportation, which makes the routing design more complicated. The existing classical optimization algorithms are difficult to meet the needs of practical applications. Therefore, the paper introduces an Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm (IMOQPSO). Then based on the continuous coding for the Railway Freight Transportation Routing Design, the proposed improved algorithm was applied to solve the problem to verify the performance of algorithm. Finally, the paper compared the performance of Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm with other four continuous multi-objective swarm intelligence algorithms. The results shown that the proposed algorithm obtained the best Pareto front which is closer to the real Pareto front of Railway Freight Transportation Routing Design. Hence, the proposed Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm can provide support for the railway transport enterprises routing design decisions to some extent.

Decomposition-based multi-objective comprehensive learning particle swarm optimisation

This paper proposes decomposition-based comprehensive learning particle swarm optimisation (DCLPSO) for multi-objective optimisation. DCLPSO uses multiple swarms, with each swarm optimising a separate objective. Two sequential phases are conducted: independent search and then cooperative search. Important information related to extreme points of the Pareto front often can be found in the independent search phase. In the cooperative search phase, a particle randomly learns from its personal best position or an elitist on each dimension. Elitists are non-dominated solutions and are stored in an external repository shared by all the swarms. Mutation is applied to each elitist in this phase to help escaping from local Pareto fronts. Experiments conducted on various benchmark problems demonstrate that DCLPSO is competitive in terms of convergence and diversity of the resulting non-dominated solutions.

Optimization of a Novel Nano Antenna With Two Radiation Modes Using Kriging Surrogate Models

In this paper, a novel nano antenna with two radiation modes is introduced. The structure of this nano antenna consists of a ring coupler and two patch antennas placed on a SiO2 substrate. The direction of the main lobe of the radiation pattern of this nano antenna can be adjusted to be either in the broadside or the endfire direction. The proposed nano antenna is optimized to minimize the losses and to maximize the radiation efficiency in addition to achieve maximum discrimination between the two desired directions of the main beam. In optimizing the proposed structure, the computationally expensive full-wave electromagnetic simulation is replaced by cheaper surrogate models, which are kriging models. Two optimization techniques, namely multi-objective particle swarm with preference ranking organization METHod for enrichment evaluations method and design centering using the normed distances, are used to obtain the optimal values of the design parameters. A convergence test is performed to ensure the validity of the obtained simulation results. A sensitivity analysis is performed to show how the manufacturing tolerance in each design parameter is affecting the performance of the proposed nano antenna.

Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints

As the low-carbon economy continues to expand, wind power, as one form of clean energy, promotes the low-carbon power development process. In this paper, a multi-objective environmental economic dispatch (EED) model is proposed considering multiple uncertainties of the system. Carbon trading costs and green certificate trading costs are introduced into the economic costs. Meanwhile, the objective function of pollutant emissions is taken into account in the model, which can further promote the reduction of pollutant emissions in the system scheduling. The output of wind turbines is uncertain and volatile, so it brings new challenges to the power system EED once the large-scale wind power accesses the power grid. For the multiple uncertainties of the system, fuzzy chance-constrained programming is introduced, and the output of the wind turbines and the load are regarded as fuzzy variables. We use the clear equivalence forms to clarify the fuzzy chance constraints. The improved multi-objective standard particle swarm optimization (SPSO) algorithm is used to solve the optimization problem effectively. The feasibility and effectiveness of the proposed model and algorithm are verified by an example of a 10-unit system with two wind farms.

Particle swarm evolutionary computation-based framework for optimizing the risk and cost of low-demand systems of nuclear power plants

ABSTRACT In this paper, an adapted multi-objective multi-swarm co-evolutionary particle swarm optimization (PSO) framework is developed to simultaneously optimize the risk and cost of low-demand systems of nuclear power plants (NPPs). In the built framework, multi-swarm co-evolutionary strategy is introduced to handle the fitness assignment puzzle of multi-objective optimization problems. Besides, to deal with the mixed-integer problem of the decision variables vector, a sub-interval covering-based nearest boundary method is also adopted. To illustrate the effectiveness and efficiencies of the proposed method, a typical high-pressurized injection system (HPIS) is analyzed. The results indicate that, compared with the classic non-dominated sorting genetic algorithm (NSGA)-II approach, the proposed method is more simple and easier to be convergent, besides, of which the Pareto front is better distributed.

Stochastic optimization methods for ship resistance and operational efficiency via CFD

Hull-form stochastic optimization methods are presented and evaluated for resistance reduction and operational efficiency (operability), addressing stochastic sea state and operations. The cost/benefit analysis of the optimization procedure is presented by comparison of four hierarchical problems, from stochastic most general to deterministic least general. The parent hull is a high-speed catamaran, with geometrical constraints for maximum variation of length, beam, draft, and displacement. Problem 1 is used as a benchmark for the evaluation of the other problem formulations and is defined as a multi-objective stochastic optimization for resistance and operability, considering stochastic sea state and speed, but limited to head waves. Problem 2 is a multi-objective stochastic optimization for resistance and motions at fixed sea state and speed. Problem 3 is a multi-objective deterministic optimization for resistance and motions using a single regular wave at fixed speed. Problem 4 is a single-objective deterministic optimization for calm-water resistance at fixed speed. The design optimization is based on hull-form modifications by the Karhunen-Loeve expansion of a free-form deformation, URANS-based CFD simulations, regular wave approximations for irregular waves, metamodels and multi-objective particle swarm. The design optimization achieves an 8.7, 23, 53, and 10% average improvements for problems 1, 2, 3, and 4, respectively. Comparing to problem 1, problem 2, 3, 4 optimized designs have average performances 1, 2.1 and 1.7% worse, respectively. The most efficient problem, from the computational cost/benefit viewpoint, is problem 3. Nevertheless, problem 1 is needed to evaluate and compare the stochastic performance of the designs and finally assess the optimization cost/benefit.

Multi-skilled project scheduling with level-dependent rework risk; three multi-objective mechanisms based on cuckoo search

In many projects, multi-skilled workforces are able to perform different tasks with different quality levels. In this paper, a real-life version of the multi-skilled resource constrained project scheduling problem is investigated, in which the reworking risk of each activity depends on the assigned level of multi-skilled workforces. The problem is formulated mathematically as a bi-objective optimization model to minimize total costs of processing the activities and to minimize reworking risks of the activities, concurrently. In order to solve the resulting problem, three cuckoo-search-based multi-objective mechanisms are developed based on non-dominance sorting genetic algorithm, multi-objective particle swarm and multi-objective invasive weeds optimization algorithm. The parameters of the algorithms are tuned using the Taguchi method to improve the efficiency of the solution procedures. Furthermore, a competitive multi-objective invasive weeds optimization algorithm is used to evaluate the performance of the proposed methodologies. Finally, a priority based method is employed to compare the proposed algorithms in terms of different metrics.

A new bi-objective periodic vehicle routing problem with maximization market share in an uncertain competitive environment

This paper presents a new variant of periodic vehicle routing problem in which the reaching time to the customers affects market share. Thus, there is a competition between distributors to achieve more market share by reaching the customers earlier than others; moreover, travel time between each two pairs of customers is uncertain. This situation is called an uncertain competitive environment. For the given problem, a new bi-objective mathematical model including minimization of total traveled time and maximization of the market share is presented. In order to solve this model, a multi-objective particle swarm (MOPSO) and local MOPSO algorithms are applied; and to evaluate the algorithm performance, some samples are generated; and the results of algorithms are compared based on some comparison metrics. The results demonstrate that the proposed LMOPSO algorithm leads to a better performance compared to the MOPSO in most comparison metrics.

A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework

Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism.