Nature-Inspired Algorithms

TL;DR: The fusion of ABC and ANN imparts the ability to address sensor noise challenges associated with system identification and parameter estimation and the capability of the proposed hybrid method to extract stability and control variables from the stable aircraft kinematics is shown.

TL;DR: This paper proposes a generalized evolutionary metaheuristic (GEM) algorithm to unify over 20 existing nature-inspired metaheuristic algorithms, addressing the lack of a unified framework and excessive algorithm proliferation in engineering optimization problems.

TL;DR: The statistical results obtained from the Wilcoxon ranking sum and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems.

TL;DR: In this article , the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and differential evolution algorithm (DE) to generate a novel hybrid Artificial Bee colony algorithm (ABC, DE-WOA), which performs better than its competitors in terms of convergence speed.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.