Differential Evolution (DE) is a widely used optimization algorithm due to its robustness and simplicity as a population search technique. However, it can struggle to balance the trade-off between exploring and exploiting the search space effectively, which is a common issue with evolutionary algorithms. To address this, this article introduces an Evolutionary Game Theory (EGT)-based DE (EGT-DE) to enhance DE's performance. EGT-DE promotes the competition among agents through the exchange of information about the search space, the modification of strategies, and the regulation of exploration and exploitation behaviors. The proposed EGT-DE algorithm was tested against nine well-known algorithms on a set of 30 benchmark functions in 50 dimensions. The Wilcoxon rank-sum test and Friedman test were used to determine the significance of the EGT -DE approach. The results showed that the proposed scheme was effective, with its performance significantly outperforming that of the other algorithms.

Evolutionary Computation (EC) algorithms have been proposed as stochastic methods to solve complex optimization problems. The design of EC methods typically involves the construction of empirical operators based on abstractions of animal behaviors or physical and biological phenomena. Through its operators, every EC approach proposes a particular solution to the exploration-exploitation balance which is currently considered an unsolved problem within EC literature. On the other hand, multi-agent systems have been utilized as intelligent, cooperative and self-organized structures where the synergy of simple rules creates complex interactions among agents. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO is based on typical processes present in multi-agent systems, such as local consensus formulations and reactive responses. These operations are conducted by using appropriate operators that are applied in each evolutionary stage. A traditional EC algorithm considers in its operation the application of every operator without examining its final impact in the searching process. In contrast to other EC techniques, the proposed method uses additional operators to avoid the undesirable effects produced by the over-exploitation or suboptimal exploration of conventional operations. In order to illustrate the performance and accuracy of the proposed NCCO approach, it is compared to several well-known, state-of-the-art algorithms over a set of benchmark functions and real-world design applications. The experimental results demonstrate that NCCO's performance is superior to the test algorithms.

A nonlinear discrete-time neural observer for discrete-time unknown nonlinear systems in presence of external disturbances and parameter uncertainties is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman-filter based algorithm. This brief includes the stability proof based on the Lyapunov approach. The applicability of the proposed scheme is illustrated by real-time implementation for a three phase induction motor.