A hybrid Jaya algorithm for reliability–redundancy allocation problems

Abstract

This paper proposes an efficient improved hybrid Jaya algorithm based on Time-Varying Acceleration Coefficients (TVAC) and learning phase introduced in Teaching-Learning-Based Optimization (TLBO), named LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer Reliability-Redundancy Allocation Problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of proposed LJaya-TVAC algorithm for finding the optimal solutions is firstly tested on the standard real-parameter unimodal and multi-model functions with dimension of 30 to 100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and best results reported in the recent literature. The obtained optimal results of proposed LJaya-TVAC algorithm provide evidence for the better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and CAES Aggregators

“…The intention of the offered method is to manage and bring together a WPA and CAES producer to be working as an HPP to contribute in three electricity markets. The uncertainties of wind power and market prices are produced with a set of scenarios using an adapted hybrid neural network and a hybrid Jaya algorithm [23].…”

“…In order to train the artificial neural network, the wind power historical data of the year 2010 are used. The scenarios related to market prices are derived by a three-step process: First, market prices are predicted for 30 days using an adapted hybrid neural network and a hybrid Jaya algorithm [22,23], and the error probability distribution function (PDF) is estimated for each hours (i.e., 24 PDFs in this study). Next, according to the estimated PDFs, immense numbers of scenarios are generated by implementing the roulette wheel mechanism.…”

“…The total negative and positive imbalances according to the amount of scheduled and actual wind power productions are formulated in (20) to (23). Constraints ( 24) and ( 25) formulates the required limitations for the CVaR calculation.…”

“…The constraints of HPP associated with the CAES model are defined previously in the CAES modeling section; see (2) to (11). The constraints of HPP associated specifically with the WPA model are also previously defined in in the WPA modeling section; see (17) to (23).…”

Rapid and Robust Adaptive Jaya (Ajaya) Based Maximum Power Point Tracking of a PV-Based Generation System

“…However, tracking time varied significantly for different PS conditions showing a lack of robustness in each of these algorithms. Hence, in this paper an adaptive jaya algorithm is proposed which keeps the initial weightage of WAC much lower using the adaptive coefficients (AC) introduced based on [39]. In each iteration the WAC value is nearer to the BEC, thereby reducing large diversifications leading to a much faster convergence compared to the simple jaya.…”