Article
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos A. Coello Coello
Summary: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic algorithm that utilizes information exchange between particles to explore the search space. This study focuses on the influence of the number of connections among particles in Multi-Objective Particle Swarm Optimizers (MOPSOs) using random regular graphs as the swarm topology. Experimental results indicate that a higher connection degree can lead to algorithm instability in various problems, and MOPSOs with the same connection degree exhibit similar behavior.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Guosen Li, Ting Zhou
Summary: This paper proposes a particle swarm optimizer based on reference point, termed RPPSO, which effectively handles global and local solutions in multimodal multi-objective optimization problems, achieving competitive performance on multiple benchmark test functions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yingying Cui, Xi Meng, Junfei Qiao
Summary: This paper proposes a multi-objective particle swarm optimization algorithm based on a two-archive mechanism (MOPSO_TA) to achieve convergence and diversity simultaneously. By using indicator-based scheme and density estimation, particles are updated and preserved to enhance solution quality. In addition, the search ability is improved through genetic operators and hybrid operators, while a flight parameters adjustment mechanism balances global exploration and local exploitation. Experimental results demonstrate the competitiveness and effectiveness of the proposed method.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
G. S. Mahapatra, B. Maneckshaw, Kash Barker
Summary: This study presents modeling of redundancy allocation under hesitant fuzzy environment as a multi-objective problem, using a mathematical framework with consideration of the system's weight and volume restrictions. A sequence of algorithms is presented to illustrate optimal solutions for improving system performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Qian Wang, Lu Chen, Xiaoguang Li, Xuexian Li
Summary: This paper proposes a new multi-objective particle swarm optimization algorithm with a dynamic neighborhood balancing mechanism (DNB-MOPSO) to solve the multi-modal multi-objective optimization problems with the same fitness value for Pareto-optimal solutions. The algorithm balances local and global search using an adaptive parameter adjustment strategy and employs a mutation operator to escape from local optima. Additionally, a dynamic neighborhood reform strategy based on current niching methods is implemented to enhance exploration and maintain population diversity. Experimental results demonstrate the superiority of the proposed algorithm in locating more optimal solutions in the decision space.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xiaoli Shu, Yanmin Liu, Jun Liu, Meilan Yang, Qian Zhang
Summary: This paper proposes a multi-objective particle swarm optimization algorithm (D-MOPSO) to solve complex multi-objective optimization problems in the real world. It addresses the lack of convergence and diversity in traditional optimization methods and makes use of existing resources in the search process. D-MOPSO dynamically adjusts the population size based on the resources in the archive, improves particle exploration through local perturbations, and controls population size through non-dominated sorting and population density.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Mechanics
Ricardo Fitas, Goncalo das Neves Carneiro, Carlos Conceicao Antonio
Summary: Optimization is a crucial area of research in Engineering that can result in cost savings and improved structural safety. Composite structures, which are often complex, require the use of the Finite Element Method for evaluation. Robust Design Optimization (RDO) is an approach that considers uncertainty in design variables or material properties to achieve robust and lightweight solutions. This study combines the advantages of Particle Swarm Optimization (PSO) with fitness assignment methodologies and elitist strategies to obtain a more perceptible Pareto front and faster optimization.
COMPOSITE STRUCTURES
(2022)
Article
Computer Science, Artificial Intelligence
Amirali Madani, Andries Engelbrecht, Beatrice Ombuki-Berman
Summary: Many real-life applications involve conflicting objectives and decision variables. Multi-guide particle swarm optimization (MGPSO) is a novel meta-heuristic that addresses multi-objective optimization problems through particle swarm optimization (PSO). A recent study found that MGPSO does not perform well when the number of decision variables increases. This paper proposes a scalable algorithm called cooperative coevolutionary multi-guide particle swarm optimization (CCMGPSO) that addresses this issue and achieves competitive results for high-dimensional problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Boyang Qu, Guosen Li, Li Yan, Jing Liang, Caitong Yue, Kunjie Yu, Oscar D. Crisalle
Summary: This paper proposes a grid-guided particle swarm optimizer for solving multimodal multi-objective optimization problems. By using a grid in the decision space, the algorithm is able to detect promising subregions and generate multiple subpopulations, maintaining diversity and improving search efficiency. Experimental results demonstrate that the proposed algorithm outperforms other evolutionary methods.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
Summary: This paper introduces a novel multi-objective particle swarm optimization feature selection method. It decodes feature vectors as particles and ranks them in a two-dimensional optimization space. The proposed method incorporates feature ranks to update particle velocity and position during the optimization process. Experimental results demonstrate the effectiveness of the method in finding Pareto Fronts of the best particles in multi-objective optimization space.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yun Hou, Guosheng Hao, Yong Zhang, Feng Gu, Wenyang Xu
Summary: This paper proposes a multi-objective discrete particle swarm optimization algorithm to solve the particle routing problem in distributed particle filters. Experimental results show that the algorithm is highly competitive and can provide multiple high-quality Pareto optimal solutions.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hailin Liu, Fangqing Gu, Zixian Lin
Summary: This study introduces a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization, which uses a multi-swarm particle swarm optimizer to solve the optimization problem. By sharing the best particle information between target and source tasks, the proposed algorithm shows effective performance across various datasets.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2021)
Article
Computer Science, Information Systems
Honggui Han, Yucheng Liu, Ying Hou, Junfei Qiao
Summary: To solve the problem of under-convergence solutions affecting particle motion and convergence in multi-modal multi-objective optimization, a self-adjusting multi-modal multi-objective particle swarm optimization (MMOPSOSS) algorithm is proposed. This algorithm promotes the convergence of multiple solution sets through parameter and population size adjustments. It incorporates a multi-swarm optimization framework, a self-adjusting local search mechanism, and a sub-swarm-balancing strategy, which are compared with other optimization algorithms in various experiments. The results show that MMOPSOSS improves the convergence of multiple solution sets for multi-modal multi-objective optimization.
INFORMATION SCIENCES
(2023)
Article
Thermodynamics
Hakan Aygun, Mehmet Kirmizi, Ulas Kilic, Onder Turan
Summary: The application of small turbojet engines is increasing due to their high power to weight ratio and reliability. This study analyzes the effects of different design variables on the performance metrics of small turbojet engines. By using multi-objective genetic algorithm, particle swarm optimization, and grey wolf optimization, the study considers several performance metrics of the engines. The findings show that increasing turbine inlet temperature improves net thrust but increases specific fuel consumption, while increasing compressor pressure ratio decreases net thrust but reduces specific fuel consumption. The optimization results suggest that different optimization methods can be utilized depending on the specific mission of the turbojet engine.
Article
Physics, Multidisciplinary
Hanlin Yang, Cunlai Pu, Jiexin Wu, Yanqing Wu, Yongxiang Xia
Summary: This paper proposes a multi-objective particle swarm optimization (MOPSO) framework to enhance the performance of the Optimized Link State Routing Protocol (OLSR) in vehicular ad hoc networks (VANETs). By considering both the quality and cost of service, the MOP is solved using MOPSO and the Pareto front is obtained. The optimization framework is applied to obtain the optimal parameters of OLSR, which are further validated in realistic VANET scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Nuclear Science & Technology
Robert Nshimirimana, Ajith Abraham, Gawie Nothnagel, Andries Engelbrecht
Summary: This paper describes a simplified approach to optimizing the radiography process using a virtual environment, utilizing ray tracing technique and particle swarm optimization routine to calculate and optimize the design parameters of the radiography system. The method provides a straightforward virtual environment for basic radiography training and experimental planning.
NUCLEAR TECHNOLOGY
(2021)
Article
Green & Sustainable Science & Technology
Shafiqur Rehman, Salman A. Khan, Luai M. Alhems
Article
Computer Science, Artificial Intelligence
A. P. Engelbrecht, P. Bosman, K. M. Malan
Summary: This study explores the correlations between the characteristics of optimization problems and the behaviors of swarm-based algorithms, revealing links between specific problem features and algorithm behaviors. The research uses fitness landscapes characteristics and diversity rate-of-change to quantify the features of problems and algorithms.
Article
Computer Science, Artificial Intelligence
Pawel Jocko, Beatrice M. Ombuki-Berman, Andries P. Engelbrecht
Summary: This study introduces archive management approaches for dynamic multi-objective optimisation problems using the multi-guide particle swarm optimisation (MGPSO) algorithm, which achieves efficient tracking of the changing Pareto-optimal front by proposing alternative archive update strategies.
SWARM INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Cian Steenkamp, Andries P. Engelbrecht
Summary: The scalability of the MGPSO algorithm for many objective optimization problems was investigated in this study. The algorithm demonstrated competitive performance across many objectives compared to other state-of-the-art algorithms, without needing specialized modifications. The use of multiple subswarms and guides in the algorithm helps balance and promote solution accuracy and diversity during the search process.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Review
Computer Science, Hardware & Architecture
Muhammad Hassan Nasir, Salman A. Khan, Muhammad Mubashir Khan, Mahawish Fatima
Summary: This paper presents a systematic review of swarm intelligence approaches deployed in intrusion detection in various attack surfaces and domains between 2010 and 2020. It categorizes the SI approaches according to their applicability in improving different aspects of intrusion detection and discusses the features of datasets used in experimentation. The study aims to help researchers assess the capabilities and limitations of SI algorithms in identifying security threats and challenges, as well as differentiating SI-based IDS from traditional ones.
Article
Chemistry, Multidisciplinary
Salman A. Khan, Kashif Iqbal, Nazeeruddin Mohammad, Rehan Akbar, Syed Saad Azhar Ali, Ammar Ahmed Siddiqui
Summary: This paper proposes a new evaluation metric for email spam detection based on fuzzy logic concept, and it confirms the effectiveness through empirical analysis and extrinsic evaluation.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Kyle Erwin, Andries Engelbrecht
Summary: Portfolio optimization is a multi-objective optimization problem that involves balancing risk and profit. Single-objective optimization methods, such as set-based particle swarm optimization (SBPSO), have been proposed to address this problem. This paper introduces the first multi-objective approach to SBPSO and compares its performance with other multi-objective algorithms. The results show that SBPSO is competitive with multiple runs, and the proposed multi-objective SBPSO achieves a more diverse set of optimal solutions.
Article
Computer Science, Artificial Intelligence
Kyle Erwin, Andries Engelbrecht
Summary: This paper provides a comprehensive review of over 140 papers that have applied evolutionary and swarm intelligence algorithms to the portfolio optimization problem. The papers are categorized based on the type of portfolio optimization problem considered and further classified into single-objective and multi-objective approaches. The various portfolio models used, as well as the constraints, objectives, and differences between them, are also discussed in detail. Based on the findings, guidance for future research in portfolio optimization is provided.
Article
Computer Science, Artificial Intelligence
Christiaan Scheepers, Andries Engelbrecht
Summary: This article investigates the shortcomings of Fonseca and Fleming's attainment surfaces and analyzes the quantitative measure based on attainment surfaces introduced by Knowles and Corne. The analysis reveals that the results of the Knowles and Corne approach are biased by the shape of the attainment surface. The article proposes improvements for bi-objective Pareto-optimal front (POF) comparisons and introduces a multi-objective optimization algorithm performance measure called the porcupine measure based on attainment surfaces. A computationally optimized version of the porcupine measure is presented and empirically analyzed.
Proceedings Paper
Computer Science, Artificial Intelligence
Stefan van Deventer, Andries Engelbrecht
Summary: A dynamic optimization method for training neural networks to predict trading behavior in the financial stock market is proposed and shown to significantly outperform static methods on a selection of South African stocks.
ADVANCES IN COMPUTATIONAL INTELLIGENCE (IWANN 2021), PT II
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
James Faure, Andries Engelbrecht
Summary: This paper proposes an approach to automate diagnosis of impacted teeth by analysing panoramic radiographs and training a convolutional neural network. Empirical results illustrate good performance in predicting impacted teeth.
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2021, PT I
(2021)
Article
Computer Science, Artificial Intelligence
Werner Mostert, Katherine M. Malan, Andries P. Engelbrecht
Summary: This study introduces a novel performance metric BFI for feature selection algorithms, which can be used for comparative analysis. The research found performance complementarity among a suite of feature selection algorithms on various real world datasets.
Article
Computer Science, Artificial Intelligence
Ryan Dieter Lang, Andries Petrus Engelbrecht
Summary: This study introduces a method to determine the minimum sample size required for robust exploratory landscape analysis measures, and utilizes self-organizing feature map to cluster a comprehensive set of benchmark functions, proposing a benchmark suite with improved coverage in single-objective, boundary-constrained problem spaces.
Article
Computer Science, Artificial Intelligence
Jonathan Mwaura, Andries P. Engelbrecht, Filipe V. Nepomuceno
Summary: This paper discusses the concepts of multimodal problems, multimodal optimisation, niche algorithms, and how diversity measures can be used to evaluate the distribution of candidate solutions and solutions of niching algorithms.
