This article presents a new population-based optimizationalgorithm to solve the multi-objective optimization problems of truss structures. This method is based on the recently developed single-solution algorithm prop...
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This article presents a new population-based optimizationalgorithm to solve the multi-objective optimization problems of truss structures. This method is based on the recently developed single-solution algorithm proposed by the present authors, so called collidingbodiesoptimization (CBO), with each agent solution being considered as an object or body with mass. In the proposed multi-objective collidingbodiesoptimization (MOCBO) algorithm, the collision theory strategy as the search process is utilized and the Maximin fitness procedure is incorporated to the CBO for sorting the agents. A series of well-known test functions with different characteristics and number of objective functions are studied. In order to measure the accuracy and efficiency of the proposed algorithm, its results are compared to those of the previous methods available in the literature, such as SPEA2, NSGA-II and MOPSO algorithms. Thereafter, two truss structural examples considering bi-objective functions are optimized. The performance of the proposed algorithm is more accurate and requires a lower computational cost than the other considered algorithms. In addition, the present methodology uses simple formulation and does not require internal parameter tuning. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.
Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly *** affect their quality of life drastically and millions of seniors are diagnosed with PD...
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Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly *** affect their quality of life drastically and millions of seniors are diagnosed with PD every year *** models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,*** identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of *** recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify *** this motivation,the current study develops a novel colliding bodies optimization algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of *** goal of the proposed CBO-OKELM technique is to identify whether PD exists or ***-OKELM technique involves the design of collidingbodiesoptimization-based Feature Selection(CBO-FS)technique for optimal subset of *** addition,Water Strider algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of *** algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic *** experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.
In this article, a maiden attempt is made to design a collidingbodiesoptimization (CBO) algorithm based proportional-integral-derivative-filter (PID-F) controller for load frequency control (LFC) of hybrid power sys...
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In this article, a maiden attempt is made to design a collidingbodiesoptimization (CBO) algorithm based proportional-integral-derivative-filter (PID-F) controller for load frequency control (LFC) of hybrid power systems. The CBO is based on collision between objects and the laws of energy and momentum, which simplifies its application compared to the several complex optimizationalgorithms. In addition to this, it does not depend on any internal parameter and provides a more straightforward formulation for minimum and maximum functions. A two-area interlinked solar photovoltaic system-reheated thermal power system is considered to establish the efficacy of the proposed approach. The controller gains are computed using CBO with the integral of time multiplied absolute error performance criteria. It is observed from the results that the proposed method significantly improves the system response and provides a better solution for the LFC problem. Finally, the optimized controller's robustness is tested by including load perturbations and parametric variations. Copyright (C) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Artificial Intelligence & Energy Systems.
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