A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle

doi:10.17559/tv-20151021202802

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…and D is the feasible domain of . Several recommendations are given in [2,[43][44][45][46] to set to set the domain D , but the most important ones concern technical and economical issues including stability constraints [50][51][52][53][54][55]. Although the stability analysis appears to be the source of many other synthesis issues, it is always important so less conservative stability conditions need to be established.…”

Section: Process Models Controller Models and Optimization Problemmentioning

confidence: 99%

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Precup¹,

David²,

Roman³

et al. 2021

IJCIS

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This paper suggests five new contributions with respect to the state-of-the-art. First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a fresh metaheuristic algorithm, namely the Slime Mould Algorithm (SMA), and a fuzzy controller tuning approach is offered. Second, a relatively easily understandable formulation of SMA is offered. Third, a real-world application of SMA is given, focusing on the optimal tuning of TSK PI-FCs for nonlinear servo systems in terms of optimization problems that target the minimization of discrete-time cost functions defined as the sum of time multiplied by squared control error. Fourth, using the concept of improving the performance of metaheuristic algorithms with information feedback models, proposed by Wang and Tan, Improving metaheuristic algorithms with information feedback models, IEEE Trans. Cybern. 49 (2019), 542-555, Gu and Wang, Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Fut. Gen. Comput. Syst. 107 (2020), 49-69, and Zhang et al., Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Inf. Sci. 522 (2020), 1-16, new metaheuristic algorithms are introduced in terms of inserting the model F1 in SMA and other representative algorithms, namely Gravitational Search Algorithm (GSA), Charged System Search (CSS), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA). Fifth, the real-time validation of the cost-effective fuzzy controllers and their tuning approach is performed in the framework of angular position control of laboratory servo system. The comparison with other metaheuristic algorithms that solve the same optimization problem for optimal parameter tuning of cost-effective fuzzy controllers suggestively highlights the superiority of SMA. Experimental results are included.

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Section: Process Models Controller Models and Optimization Problemmentioning

confidence: 99%

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Precup¹,

David²,

Roman³

et al. 2021

IJCIS

View full text Add to dashboard Cite

show abstract

“…Particle swarm optimization (PSO) is one of the most popular nature-inspired optimization algorithms because of its simplicity and ease of use [80,81]. This algorithm is inspired by the movement of birds and fish in their groups, i.e., the social behavior of feeding birds and fish [82,83].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Review of Nature-Inspired Optimization Algorithms Applied in Civil Engineering

Obradović

2018

e-GFOS

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Nature has always been an example of perfection and inspiration. In nature, everything has reasons why it is happening exactly the way it does. Nature-inspired optimization algorithms have become a rapidly growing area of research in all areas of life. Ant colonies find the shortest path to food, the evolution of the living world shows adaptation to the world around it. For example, bees find the optimal path to food and back to the hive. Optimization algorithms contribute significantly to solving many complex issues and achieving optimal results. This research paper outlines nature-inspired optimization algorithms, such as ant colonies, artificial immune system s, artificial neural networks, flocks of bats, bee swarms, firefly algorithms, genetic algorithms, and particle swarms. The purpose of this brief overview is to provide an easy-to-understand list of the basic features of the most common nature-inspired optimization algorithms as well as the potential applications of the aforementioned algorithms in civil engineering.

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“…Biologically inspired path planning algorithms use principles, which belong to the computational intelligence like, for instance, bug algorithms, ant colony algorithms [20], neural networks, particle swarm optimization [21], or fuzzy logic. For more details, see, for example, [22].…”

Section: Introductionmentioning

confidence: 99%

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

et al. 2021

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Development of accessible and cheap sensors as well as the possibility to transfer and process huge amounts of data offer new possibilities for many areas utilizing till now conventional approaches. Navigation of robots and autonomous vehicles is no exception in this aspect and Internet of Things (IoT), together with the means of computational intelligence, represents a new way for construction and use of robots. In this paper, the possibility to move sensors from robots to their surroundings with the help of IoT is presented and the modification of the IoT concept in the form of intelligent space as well as the concept of ubiquitous robot are shown in the paper. On an example of route tracking, we will clarify the potential of distributed networked sensors and processing their data with the use of fuzzy cognitive maps for robotic navigation. Besides, two modifications of adaptation approaches, namely particle swarm optimization and migration algorithm, are presented here. A series of simulations was performed, which are discussed and future research directions are proposed.

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A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle

Cited by 13 publications

References 27 publications

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Review of Nature-Inspired Optimization Algorithms Applied in Civil Engineering

Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots

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