Handling Multiple Objectives With Particle Swarm Optimization,

Handling Multiple Objectives With Particle Swarm Optimization, Based on the existing single objective-based CPAs, a modified multi-objective NSCPA is first developed for multi-objective planning optimization using the non-dominated sorting algorithm. May 6, 2025 · In this paper, we propose a novel multi-objective particle swarm optimization algorithm with a task allocation and archive-guided mutation strategy (TAMOPSO), which effectively solves the Single objective optimization has only one objective function, while multiobjective optimization has multiple objective functions that generate the Pareto set. Jul 1, 2004 · This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several May 1, 2022 · In this paper, we propose an enhanced multi-objective particle swarm optimization (EMOPSO) method which uses Lévy flight to enhance exploration and expedite the search to obtain multiple global optima. This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) to address multiobjective optimization problems. The goal of this paper is to provide a comprehensive Jan 19, 2023 · 三、多目标粒子群优化算法（Multiple Objective Particle Swarm Optimization，MOPSO） 多目标粒子群算法由 Coello Coello等人于2002年提出（网上很多文章说是2004年提出的，但我能找到的最早论文是2002年，详见参考文献 [3]）。 MOPSO的粒子速度和位置的更新公式如下： 速度更新 . This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. The proposed algorithm, called multiobjective particle swarm optimization (MOPSO), utilizes a secondary repository of particles and includes a mutation operator to enhance exploration. Apr 16, 2025 · In the rapidly evolving field of aerial robotics, the coordinated management of multiple unmanned aerial vehicle (multi-UAV) systems to address complex and dynamic environments is increasingly critical. First, being derivative-free, PSO accommodates complex, non-differentiable simulation-based fitness evaluations where analytical gradients are unavailable. Mar 1, 2024 · To address the limitations of single-objective solution algorithms and the lack of diversity and premature convergence in multi-objective optimization processes, a multi-objective particle swarm optimization by multi-strategy improvements (CMOPSO-MSI) is proposed to solve the model. 5 days ago · The rapid deployment of Fifth Generation 5G networks has intensified the challenges in antenna layout optimization, which requires balancing multiple conflicting objectives including signal coverage, energy consumption, interference suppression, and deployment cost. 7% respectively compared to the particle swarm optimization algorithm. Then, the analytical hierarchy process is used to choose the best service 1 day ago · Download Citation | Machine learning-enhanced metaheuristic optimization of lead rubber bearings for inter-story isolated buildings under seismic load | This paper presents a machine learning Semantic Scholar extracted view of "Multiobjective particle swarm optimization for environmental/economic dispatch problem" by M. Classic examples of global planners include A* algorithm, Visibility Graph, Voronoi Diagram, and optimization-based methods like Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) when applied to a known static map. Therefore, to solve multiobjective problems is a challenging task. Nowadays, it is becoming increasingly clear that MOPSO can handle with complex MOPs based on the competitive-cooperative framework. Mar 18, 2025 · In order to solve complex optimization problems, swarm intelligence (SI) techniques that draw inspiration from the collective behavior of fish schools, ant foraging, and bird flocking are gaining popularity. Unlike other current proposals to extend PSO to The optimization problem minimizes the total operational cost and pollutant emissions simultaneously, employing a modified particle swarm optimization algorithm enhanced with adaptive control and dynamic mutation strategies. Handling Multiple Objectives With Particle Swarm Optimization Carlos A. The study uses well logging data from the Yanchang Formation of the Changqing Oilfield in the Ordos Basin. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two widely recognized techniques in the fields of metaheuristics. A. Dec 20, 2025 · A Novel Combination of Genetic Algorithm, Particle Swarm Optimization, and Teaching-Learning-Based Optimization for Distribution Network Reconfiguration in Case of Faults Article Full-text available 3 days ago · However, it lacks the flexibility to handle unexpected dynamic obstacles or real-time environmental changes. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. The algorithm takes advantage of the exploration and exploitation abilities of both methods and outperforms other state-of-the-art evolutionary algorithms on several benchmark functions. Abido 4 days ago · Abstract In the field of modern science and engineering, constrained multi-objective optimization problems (CMOPs) generally involve multiple conflicting optimization objectives and need to satisfy various constraint conditions. Traditional optimization methods often struggle to handle the complex trade-offs between these objectives while satisfying A task conflict detection and resolution method is proposed to handle the task assignment among multiple satellites. 4 days ago · In this paper, we introduce the attention mechanism into a particle swarm optimizer and propose an attention-based particle swarm optimizer (APSO) for large scale optimization. The hybrid model, termed PSO-LSTM-XGB, employs the particle swarm optimization (PSO) algorithm to fine-tune hyperparameters, enhancing prediction accuracy. The study uses well logging data from the Yanchang Formation of the Changqing Oilfield in the Ordos Basin. Abstract: This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. For multi-objective task scheduling in cloud computing, the second layer uses a whale optimization technique based on the Gaussian cloud model (GCWOAS2). How to effectively handle the constraint conditions is crucial for improving the optimization effect. Abstract: In the last decade, multiobjective particle swarm optimization (MOPSO) has been observed as one of the most powerful optimization algorithms in solving multiobjective optimization problems (MOPs). Jun 1, 2004 · This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Particle swarm optimization (PSO) showed the least accuracy (54), compared to other algorithms. 4% and 7. 4 days ago · This paper presents a new particle swarm optimizer for solving multimodal multi-objective optimization problems which may have more than one Pareto-optimal solution corresponding to the same 2 days ago · A particle swarm optimization (PSO)-based intelligent scheduling algorithm is established, with queuing time, skill level, and handling time as key objectives and constraints. MOPSOEO combines particle swarm optimization (PSO) with extremal optimization (EO) to solve multiobjective optimization problems (MOPs). Coello Coello, Member, IEEE, Gregorio Toscano Pulido, and Maximino Salazar Lechuga 1 day ago · The hybrid model, termed PSO-LSTM-XGB, employs the particle swarm optimization (PSO) algorithm to fine-tune hyperparameters, enhancing prediction accuracy. In conclu-sion, Tabu search and genetic algorithms showed the highest accuracy at 98% and 94%, respectively. 1 day ago · Given these limitations, Particle Swarm Optimization is adopted as the core framework due to distinctive advantages tailored to our problem structure. This article provides a comprehensive examination Dec 1, 2025 · In the intra-farmland path planning, the ILA optimization algorithm improves the rebroadcast rate and reduces the turn times by 6. Jul 1, 2004 · This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. dofql5, ze3i, hguuf, lfvx, fsfon, fkpds, zrzz7o, ks35h, nxgx, rocui9,