An accessible introduction to metaheuristics and optimization, featuring powerful and
modern algorithms for application across engineering and the sciences From engineering …

Modern metaheuristic algorithms such as particle swarm optimization and cuckoo search
start to demonstrate their power in dealing with tough optimization problems and even NP …

This paper introduces a data-driven approach for fault diagnosis in the presence of
incomplete monitor data. The expectation-maximization (EM) algorithm is applied to handle …

A model of honey bee social foraging is introduced to create an algorithm that solves a class
of dynamic resource allocation problems. We prove that if several such algorithms (“hives”) …

In this paper, we propose a robust multiple-model linear parameter varying (LPV) approach
to identification of the nonlinear process contaminated with outliers. The identification …

The creation of new technologies that serve humanity holds the potential to help end global
poverty. Unfortunately, relatively little is done in engineering education to support engineers' …

This paper proposes an improved interacting multiple-model (I 2 MM) algorithm with
inaccurate transition probabilities (TPs) for fault detection and diagnosis (FDD). We first …

This paper presents a hybrid optimization algorithm which originates from Invasive Weed
Optimization (IWO) and Particle Swarm Optimization (PSO). Based on the novel and distinct …

In this paper, a robust approach to process monitoring and diagnosis based on hidden
Markov models with time-varying transition probabilities is proposed. We model …

We extend the theory of the" ideal free distribution"(IFD) from theoretical ecology by
providing methods to analytically find the distribution for a relatively general class of" …