Simplified Swarm Optimization Research Articles

A simplified swarm optimization algorithm to minimize makespan on non-identical parallel machines with unequal job release times under non-renewable resource constraints

A simplified swarm optimization algorithm to minimize makespan on non-identical parallel machines with unequal job release times under non-renewable resource constraints

A novel automatic detection and classification algorithm for elderly cognitive impairment using CSVM

Alzheimer’s disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely.

Risk-averse two-stage stochastic programming-based closed-loop supply chain network design under uncertain demand

In recent years, there has been a growing awareness and acceptance of the concept of green environmental protection among the public, primarily due to the worsening environmental issues and increased emphasis by governments on corporate social responsibility regulations. As a result, the management of closed-loop supply chains has gained significant attention from both businesses and researchers. Most stochastic programming models addressing closed-loop supply chain (CLSC) under uncertainty are typically designed to be risk-neutral. In this paper, a risk-averse two-stage stochastic programming (RATSSP) model for the closed-loop supply chain network design (CLSCND) problem considering facility capacity level decisions under uncertain demand. It is aiming to trade off the expected total profit and the profit risk, based on a given degree of responsive risk aversion. Due to the NP-hard nature of the CLSCND problem, this paper applies simplified swarm optimization (SSO), genetic algorithm (GA), and particle swarm optimization (PSO) algorithms to solve the problem. To improve the efficiency and quality of the obtained solutions, we propose a parallel computation meta-heuristic algorithm, parallel simplified swarm optimization (PSSO), along with parallel versions of PSO and GA. Computational results show the effectiveness of our proposed model, with PSSO demonstrating the best robustness and solution quality among the considered algorithms.

A novel binary-addition simplified swarm optimization for generalized reliability redundancy allocation problem

Abstract Network systems are commonly used in various fields, such as power grids, Internet of Things, and gas networks. The reliability redundancy allocation problem is a well-known reliability design tool that needs to be developed when the system is extended from a series-parallel structure to a more general network structure. Therefore, this study proposes a novel reliability redundancy allocation problem, referred to as the general reliability redundancy allocation problem, to be applied in network systems. Because the general reliability redundancy allocation problem is NP-hard, a new algorithm referred to as binary-addition simplified swarm optimization is proposed in this study. Binary-addition simplified swarm optimization combines the accuracy of the binary addition tree algorithm with the efficiency of simplified swarm optimization, which can effectively reduce the solution space and speed up the time required to find high-quality solutions. The experimental results show that binary-addition simplified swarm optimization outperforms three well-known algorithms: the genetic algorithm, particle swarm optimization, and simplified swarm optimization in high-quality solutions and high stability on six network benchmarks.

Cloud Computing Considering Both Energy and Time Solved by Two-Objective Simplified Swarm Optimization

Cloud computing is an operation carried out via networks to provide resources and information to end users according to their demands. The job scheduling in cloud computing, which is distributed across numerous resources for large-scale calculation and resolves the value, accessibility, reliability, and capability of cloud computing, is important because of the high development of technology and the many layers of application. An extended and revised study was developed in our last work, titled “Multi Objective Scheduling in Cloud Computing Using Multi-Objective Simplified Swarm Optimization MOSSO” in IEEE CEC 2018. More new algorithms, testing, and comparisons have been implemented to solve the bi-objective time-constrained task scheduling problem in a more efficient manner. The job scheduling in cloud computing, with objectives including energy consumption and computing time, is solved by the newer algorithm developed in this study. The developed algorithm, named two-objective simplified swarm optimization (tSSO), revises and improves the errors in the previous MOSSO algorithm, which ignores the fact that the number of temporary nondominated solutions is not always only one in the multi-objective problem, and some temporary nondominated solutions may not be temporary nondominated solutions in the next generation based on simplified swarm optimization (SSO). The experimental results implemented show that the developed tSSO performs better than the best-known algorithms, including nondominated sorting genetic algorithm II (NSGA-II), multi-objective particle swarm optimization (MOPSO), and MOSSO in the convergence, diversity, number of obtained temporary nondominated solutions, and the number of obtained real nondominated solutions. The developed tSSO accomplishes the objective of this study, as proven by the experiments.

Simplified swarm optimization for hyperparameters of convolutional neural networks

Convolutional neural networks (CNNs) are widely used in image recognition. Numerous CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have been developed by increasing the number of layers to improve performance. However, performance deteriorates beyond a certain number of layers. Hence, hyperparameter optimization is a more efficient way to improve CNNs. To validate this concept, in the present study, an algorithm based on simplified swarm optimization was developed for optimizing the hyperparameters of the simplest CNN model: LeNet. The results of experiments involving the MNIST, Fashion-MNIST, and CIFAR-10 datasets indicated that the accuracy of the proposed algorithm was higher than those of LeNet and PSO-LeNet and that the proposed algorithm can be applied to more complex models such as AlexNet.

Pseudo Computed Tomography estimation from brain MRI using anatomic signature and joint dictionary learning

Most research on using pseudo-computed tomography (pCT) on brain-imaging techniques relies on in-house methods. As performance as a whole increase, they pay particular attention when using MRI imaging. Methodologies for predicting CT values from MRI data are needed in radiation treatment (RT). Although the employment of dictionary-learning-based approaches for defining picture patches has not been considered, it has been found that Deep Learning (DL) offers increased opportunities in the medical domains. The stages of this paper CT estimation from MRI using Anatomic Signature and Joint Dictionary Learning (ASJDL) are as follows: a) data gathering from the RIRE image data and b) image pre-processing to remove anomalies. c) Using Gabor Filters for feature extraction to extract significant features d) The choice of anatomic signature traits is used to identify strong, illuminating characteristics that classify and identify objects. e) Development of the pCT through shared dictionary learning. Using cutting-edge techniques like Accelerated Simplified Swarm Optimization (ASSO), Particle Swarm Optimization (PSO), Simplified Swarm Optimization (SSO), SLA12, Intensity-based, Fast-patch based, and Coupled dictionary over measures like peak S/N ratio, MAE, normalized cross-correlation, SSIM, Accuracy, and Computation time, the evaluation of both feature selection and classification methods is compared. With values of 23.825.08 (PSNR), 83.327.05 (MAE(HU)), 0.920.03 (NCC), and 0.860.03 (SSIM), the suggested framework (ASJDL) performs better than all other methods.

Bi-Objective simplified swarm optimization for fog computing task scheduling

In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale.

The application of simplified swarm optimization in a precautionary evacuation model

This paper introduces a two-level covering problem in the pre-positioning of military forces for precautionary evacuation during a typhoon, which is called the precautionary evacuation covering problem (PECP). PECP is highly constrained by multiple practical constraints, and requires a long time to converge to the optimal or near-optimal solution, as the evolutionary method used to solve it often encounters a population of infeasible solutions. This study proposes a tailored simplified swarm optimization method (SSO) with two novel mechanisms to solve this problem, and utilizes the problem-specific knowledge of PECP to accelerate the solution process and improve the solution quality. The proposed approach (SSOR&D) is empirically verified on a set of randomly generated problems, and compared with widely popular evolutionary approaches. SSOR&D can reduce the average of fitness value by at least 26% performed better than original SSO owing to the two proposed mechanisms. Compared to three other evolutionary methods and their improved versions, SSOR&D outperformed 26 out of 36 instances of PECP in terms of the average of fitness values. Overall, the computational results demonstrate that the proposed approach is highly competitive and performs well in terms of solution quality and convergence rate.

A Mixed-Heuristic Quantum-Inspired Simplified Swarm Optimization Algorithm for scheduling of real-time tasks in the multiprocessor system

Real-time task scheduling in multiprocessor (MP) systems has become more critical. As quantum computing advances, scholars have proposed different Quantum-Inspired (QI) Optimization Algorithms to optimize real-time task scheduling problems. In this research, we first take advantage of the Simplified Swarm Optimization Algorithm (SSO) and then propose a Quantum-Inspired Simplified Swarm Optimization (QISSO) to not only overcome the early convergence problem but also enhance the exploration of solution space without premature. Furthermore, we propose the Mixed-Heuristic QISSO for real-time task scheduling in the MP system to improve the scheduling. The proposed MHQISSO achieves the best average percentage of success within the shortest running time in five different size task sets according to two scenarios, Earliest Deadline First (EDF) and Shortest Computational Time First (SCTF).

A Hybrid Algorithm Based on Simplified Swarm Optimization for Multi-Objective Optimizing on Combined Cooling, Heating and Power System

Energy demand is rising sharply due to the technological development and progress of modern times. Neverthless, traditional thermal power generation has several diadvantages including its low energy usage and emitting a lot of polluting gases, resulting in the energy depletion crisis and the increasingly serious greenhouse effect. In response to environmental issues and energy depletion, the Combined Cooling, Heating and Power system (CCHP) combined with the power-generation system of renewable energy, which this work studied, has the advantages of high energy usage and low environmental pollution compared with traditional thermal power generation, and has been gradually promoted in recent years. This system needs to cooperate with the instability of renewable energy and the dispatch of the energy-saving system; the optimization of the system has been researched recently for this purpose. This study took Xikou village, Lieyu township, Kinmen county, Taiwan as the experimental region to solve the optimization problem of CCHP combined with renewable energy and aimed to optimize the multi-objective system including minimizing the operation cost, minimizing the carbon emissions, and maximizing the energy utilization rate. This study converted the original multi-objective optimization problem into a single-objective optimization problem by using the Technique for Order Preference by Similarity to and Ideal Solution (TOPSIS) approach. In addition, a hybrid of the simplified swarm optimization (SSO) and differential evolution (DE) algorithm, called SSO-DE, was proposed in this research to solve the studied problem. SSO-DE is based on SSO as the core of the algorithm and is combined with DE as the local search strategy. The contributions and innovations of the manuscript are clarified as follows: 1. a larger scale of CCHP was studied; 2. the parallel connection of the mains, allowing the exchange of power with the main grid, was considered; 3. the TOPSIS was adopted in this study to convert the original multi-objective optimization problem into a single-objective optimization problem; and 4. the hybrid of the DE algorithm with the improved SSO algorithm was adopted to improve the efficiency of the solution. The proposed SSO-DE in this study has an excellent ability to solve the optimization problem of CCHP combined with renewable energy according to the Friedman test of experimental results obtained by the proposed SSO-DE compared with POS-DE, iSSO-DE, and ABC-DE. In addition, SSO-DE had the lowest running time compared with POS-DE, iSSO-DE, and ABC-DE in all experiments.

A hybrid feature selection algorithm using simplified swarm optimization for body fat prediction

Background and objectivesObesity is one of the chronic diseases that seriously threaten people's health outcomes globally. Since the prevalence of obesity is increasing among people of all ages, measuring the body fat percentages is vital before treatment. However, the body fat percentage cannot be accurately measured by weighing. While many devices are commonly used to measure the body fat percentage, these devices are expensive and depend on complex instruments. Therefore, more practical and cost-effective solutions are desired to measure body fat accurately. This study presents a hybrid feature selection method based on a VIKOR-based multi-filter ensemble technique (VMFET) and an improved simplified swarm optimization (iSSO) to predict the body fat percentage with low prediction error. MethodsThe study followed a two-phase process. First, VMFET was used to aggregate the statistical outcomesof individual filters to filter the most informative features from the original dataset. Then, the selected features are applied to the next phase. Second, iSSO was tailored with a biased random initialization scheme, effect-based feature pruning scheme, and multiple linear regression as a wrapper method to improve the prediction performance and select the optimal feature subset. Studies resultsExtensive experiments were performed using nine datasets to verify the performance of the proposed method empirically, and the corresponding results were compared with up-to-date studies. ConclusionThe statistical results demonstrated that the proposed method offers a promising and effective tool for predicting body fat. SignificanceThe hybrid feature selection model can enhance prediction accuracy and lower prediction error.

ANN and SSO Algorithms for a Newly Developed Flexible Grid Trading Model

In the modern era, the trading methods and strategies used in the financial market have gradually changed from traditional on-site trading to electronic remote trading, and even online automatic trading performed by pre-programmed computer programs. This is due to the conduct of trading automatically and self-adjustment in financial markets becoming a competitive development trend in the entire financial market, with the continuous development of network and computer computing technology. Quantitative trading aims to automatically form a fixed and quantifiable operational logic from people’s investment decisions and apply it to the financial market, which has attracted the attention of the financial market. The development of self-adjustment programming algorithms for automatically trading in financial markets has transformed to being a top priority for academic research and financial practice. Thus, a new flexible grid trading model incorporating the Simplified Swarm Optimization (SSO) algorithm for optimizing parameters for various market situations as input values and the Fully Connected Neural Network (FNN) and Long Short-Term Memory (LSTM) model for training a quantitative trading model for automatically calculating and adjusting the optimal trading parameters for trading after inputting the existing market situation are developed and studied in this work. The proposed model provides a self-adjust model to reduce investors’ effort in the trading market, obtains outperformed Return of Investment (ROI) and model robustness, and can properly control the balance between risk and return.

Disassembly sequence planning and application using simplified discrete gravitational search algorithm for equipment maintenance in hydropower station

Disassembly sequence planning (DSP) is a crucial way to optimize equipment maintenance process for hydropower equipment (HE). As a discrete combinatorial optimization problem with complex disassembly precedence constraints, however, the serious combination explosion caused by a large number of HE components will make the solution process difficult. Thus, a simplified discrete gravitational search algorithm (SDGSA) is proposed based on the core idea of the gravitational search algorithm (GSA). In the proposed algorithm, fast feasible solution generator (FFSG) is used to generate the initial population, precedence preservative operator (PPO) is utilized to generate the next generation efficiently, multipoint optimization operator (MOO) is applied to guide a solution to move to the better neighbor solution, and escaping operator (EO) is employed to prevent population falling into local optimum prematurely and improve the ability to find the optimal solution. In this study, the performance comparison experiments are carried out among SDGSA, simplified swarm optimization (SSO) algorithm, genetic algorithm (GA), simplified teaching-learning-based optimization (STLBO), and Team-Based Genetic Algorithm (TBGA). The results of the three maintenance tasks with different complexity in the flat plate water seal (FPWS) shows that in the solution of case 1-case 3, the proportion of the optimal sequence found by the SDGSA is 13.3 %, 70 %, and 70 % higher than comparison algorithms, respectively. The convergence speed and optimization ability are also better than other algorithms. Finally, the proposed method has been successfully applied to the automatic generation of virtual operation instruction (VOI) for equipment maintenance.

(Retracted) Hybrid whale optimization algorithm-Levy flight approach for multilevel thresholding image segmentation

Determining the optimal threshold for multilevel image segmentation is a stimulating task. Image segmentation has many applications, such as content-based image retrieval, medical imaging, object detection, object recognition, machine vision, etc. In multilevel thresholding, multiple thresholds are used to segment complex images, and the images are segmented into multiple levels to extract meaningful information for further image analysis. Where threshold number is small, p-tile, Otsu, moment preserving, and entropy thresholding methods attain good accuracy. These classical thresholding methods are time-consuming, computation expensive, and unable to produce good segmentation accuracy with increased threshold numbers. To overcome these problems, the classical thresholding methods are utilized as objective functions with nature-inspired metaheuristic algorithms such as whale optimization algorithm (WOA), simplified swarm optimization (SSO), sine cosine algorithm (SCA), BAT algorithm, WOA-thresholding, and black widow optimization (BWO) to determine optimal multiple thresholds. Nature-inspired metaheuristic algorithms are widely used to search for an optimal solution for global optimization problems. WOA is widely used to find the optimal solution in search space. WOA suffers from entrapment into local optima due to premature convergence behavior. We introduce hybridization of WOA with Levy flight trajectory and named as hybrid whale optimization algorithm-Levy flight (HWOAL), which is utilized to find optimal multiple thresholds for multilevel image segmentation. Levy flight trajectory is utilized to increase diversity in the swarm population. The efficacy of HWOAL is tested on 23 benchmark optimization functions (F1 to F23) and compared with WOA, SSO, SCA, and BAT algorithms. Experiment arms that HWOAL is efficient and makes WOA faster, enhances the ability of exploitation and exploration phase, and can avoid getting stuck into local optima. The segmentation performance of the proposed HWOAL method has been compared with other algorithms, such as WOA, SSO, SCA, BAT, and other two recent segmentation algorithms, such as WOA-TH and BWO on several benchmark images (BSD 300). The experimental result is carried in 30 trials and analyzed based on objective fitness value, optimal multilevel threshold values, segmentation quality measures, such as mean square error, peak signal-to-noise ratio, structural similarity index, average difference, and computation time to compute optimal multilevel threshold values. The experiment conducted by the authors shows that the proposed HWOAL method is efficient, produces better fitness value, and segmentation metrics for multilevel image segmentation than other algorithms.

Solving Dual-Channel Supply Chain Pricing Strategy Problem with Multi-Level Programming Based on Improved Simplified Swarm Optimization

With the evolution of the Internet and the introduction of third-party platforms, a diversified supply chain has gradually emerged. In contrast to the traditional single sales channel, companies can also increase their revenue by selling through multiple channels, such as dual-channel sales: adding a sales channel for direct sales through online third-party platforms. However, due to the complexity of the supply chain structure, previous studies have rarely discussed and analyzed the capital-constrained dual-channel supply chain model, which is more relevant to the actual situation. To solve more complex and realistic supply chain decision problems, this paper uses the concept of game theory to describe the pricing negotiation procedures among the capital-constrained manufacturers and other parties in the dual-channel supply chain by applying the Stackelberg game theory to describe the supply chain structure as a hierarchical multi-level mathematical model to solve the optimal pricing strategy for different financing options to achieve the common benefit of the supply chain. In this study, we propose a Multi-level Improved Simplified Swarm Optimization (MLiSSO) method, which uses the improved, simplified swarm optimization (iSSO) for the Multi-level Programming Problem (MLPP). It is applied to this pricing strategy model of the supply chain and experiments with three related MLPPs in the past studies to verify the effectiveness of the method. The results show that the MLiSSO algorithm is effective, qualitative, and stable and can be used to solve the pricing strategy problem for supply chain models; furthermore, the algorithm can also be applied to other MLPPs.

Designing a reliable hierarchical military logistic network using an improved simplified swarm optimization

This study presents a novel reliable hierarchical facility location model addressing a real-world military logistic network design problem, where facilities are subject to the risk of disruptions. It views the proposed model from a system reliability perspective. The reliability for a battalion is interpreted as the probability of required supplies delivered to its combat units via its primary and backup depots. In addition, this study seeks to design a military logistic network with stated reliability and pursues to minimize the total cost. An improved simplified swarm optimization (iSSO) with two novel mechanisms was developed to solve the proposed model due to the NP-hardness of the problem under consideration. The efficiency for solving small-scale problems via the comparison of results with CPLEX is investigated. Furthermore, the performance of the presented iSSO is compared with widely popular evolutionary algorithms on large-scale problems. Comprehensive analysis indicates that iSSO is highly competitive and performs well in terms of solution quality and convergence rate.

Applying Hybrid Genetic Simplified Swarm Optimization for Distributed Flexible Job-Shop Scheduling Problem

Applying Hybrid Genetic Simplified Swarm Optimization for Distributed Flexible Job-Shop Scheduling Problem

A Novel Constraints Model of Credibility-Fuzzy for Reliability Redundancy Allocation Problem by Simplified Swarm Optimization

A novel constraints model of credibility-fuzzy for the reliability redundancy allocation problem (RRAP) is studied in this work. The RRAP that must simultaneously decide reliability and redundancy of components is an effective approach in improving the system reliability. In practice various systems, the uncertainty condition of components used in the systems, which few studies have noticed this state over the years, is a concrete fact due to several reasons such as production conditions, different batches of raw materials, time reasons, and climatic factors. Therefore, this study adopts the fuzzy theory and credibility theory to solve the components uncertainty in the constraints of RRAP including cost, weight, and volume. Moreover, the simplified swarm optimization (SSO) algorithm has been adopted to solve the fuzzy constraints of RRAP. The effectiveness and performance of SSO algorithm have been experimented by four famous benchmarks of RRAP.

A Novel Bi-Tuning SSO Algorithm for Optimizing the Budget-Limited Sensing Coverage Problem in Wireless Sensor Networks

The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work.