Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Pires, E. J. Solteiro; Machado, J. A. Tenreiro; Oliveira, P. B. de Moura

doi:10.3390/e21090827

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…This evolution has enabled them to merge theoretical principles from other science fields. For instance, Shannon entropy [ 21 , 22 , 23 ] has been used in a population distribution strategy based on historical information [ 41 ]. The study reveals a close relationship between the solutions’ diversity and the algorithm’s convergence.…”

Section: Related Workmentioning

confidence: 99%

Entropy–Based Diversification Approach for Bio–Computing Methods

Olivares

Soto

Crawford

et al. 2022

Entropy

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Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.

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Section: Related Workmentioning

confidence: 99%

Entropy–Based Diversification Approach for Bio–Computing Methods

Olivares

Soto

Crawford

et al. 2022

Entropy

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“…In this area of computer science, evolutionary computation provides a framework for optimization schemes inspired by biological evolution [22][23][24][25][26]. In general, evolutionary computation leads to a family of population-based optimization algorithms [27,28], with a numerical trial and error meta-heuristic and probabilistic behavior [29][30][31][32][33]. An initial set of elements in a population (often called candidate solutions) are generated and successively updated by means of some logical rules including some random variations.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Perspective of Virus Propagation

Machado

2020

Mathematics

Self Cite

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This paper presents an evolutionary algorithm that simulates simplified scenarios of the diffusion of an infectious disease within a given population. The proposed evolutionary epidemic diffusion (EED) computational model has a limited number of variables and parameters, but is still able to simulate a variety of configurations that have a good adherence to real-world cases. The use of two space distances and the calculation of spatial 2-dimensional entropy are also examined. Several simulations demonstrate the feasibility of the EED for testing distinct social, logistic and economy risks. The performance of the system dynamics is assessed by several variables and indices. The global information is efficiently condensed and visualized by means of multidimensional scaling.

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Abstract Rotations for Uniform Adaptive Control and Soft Modeling of Mechanical Devices

et al. 2021

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The model-based controllers generally suffer from the lack of precise dynamic models. Making reliable analytical models can be evaded by soft modeling techniques, while the consequences of modeling imprecisions are tackled by either robust or adaptive techniques. In robotics, the prevailing adaptive techniques are based on Lyapunov’s “direct method” that normally uses special error metrics and adaptation rules containing fragments of the Lyapunov function. The soft models and controllers need massive parallelism and suffer from the curse of dimensionality. A different adaptive approach based on Banach’s fixed point theorem and using special abstract rotations was recently suggested. Similar rotations were suggested to develop particular neural network-like soft models, too. Presently, via integrating these approaches, a uniform adaptive controlling and modeling methodology is suggested with especial emphasis on the effects of the measurement noises. Its applicability is investigated via simulations for a two degree of freedom mechanical system in which one of the generalized coordinates is under control, while the other one belongs to a coupled parasite dynamical system. The results are promising for allowing the development of relatively coarse soft models and a simple adaptive rule that can be implemented in embedded systems.

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Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Cited by 3 publications

References 27 publications

Entropy–Based Diversification Approach for Bio–Computing Methods

Entropy–Based Diversification Approach for Bio–Computing Methods

An Evolutionary Perspective of Virus Propagation

Abstract Rotations for Uniform Adaptive Control and Soft Modeling of Mechanical Devices

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