scholarly journals Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

2021 ◽  
Author(s):  
Yiyong Sun ◽  
Haotian Zhao ◽  
Zhang Chen ◽  
Xudong Zheng ◽  
Mingguo Zhao ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Particle Swarm Optimization ◽  
Balance Control ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Modified Particle Swarm Optimization ◽  
Model Based

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

scholarly journals A Multi-Objective Permanent Basic Farmland Delineation Model Based on Hybrid Particle Swarm Optimization

2020 ◽  
Vol 9 (4) ◽  
pp. 243 ◽  
Author(s):  
Hua Wang ◽  
Wenwen Li ◽  
Wei Huang ◽  
Ke Nie
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Research Area ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Model Based ◽  
Hybrid Particle Swarm Optimization ◽  
General Rules ◽  
Set Up

The delimitation of permanent basic farmland is essentially a multi-objective optimization problem. The traditional demarcation methods cannot simultaneously take into account the requirements of cultivated land quality and the spatial layout of permanent basic farmland, and it cannot balance the relationship between agriculture and urban development. This paper proposed a multi-objective permanent basic farmland delimitation model based on an immune particle swarm optimization algorithm. The general rules for delineating the permanent basic farmland were defined in the model, and the delineation goals and constraints have been formally expressed. The model introduced the immune system concepts to complement the existing theory. This paper describes the coding and initialization methods for the algorithm, particle position and speed update mechanism, and fitness function design. We selected Xun County, Henan Province, as the research area and set up control experiments that aligned with the different targets and compared the performance of the three models of particle swarm optimization (PSO), artificial immune algorithm (AIA), and the improved AIA-PSO in solving multi-objective problems. The experiments proved the feasibility of the model. It avoided the adverse effects of subjective factors and promoted the scientific rationality of the results of permanent basic farmland delineation.

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