In the last couple of decades, nature-inspired meta-heuristic algorithms have increasingly become a dominant class of tools for solving a range of complex and high-dimensional optimization problems. Some examples of these algorithms are Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithms (GA) or its more advanced version, Differential Evolutionary (DE) Algorithm, along with Bat, Cuckoo and Firefly Algorithms, just to name a few. These algorithms are motivated by animal natural behavior, they are relatively easy to program, implement and easy to adjust for solving different types of optimization problems. They are assumptions free and generally solve or nearly solve the optimization problem quickly using an iterative and stochastic search. Typically a rigorous proof of their convergence is lacking but there are pseudo or partial proofs that require more technical assumptions on the optimization problems than their original version.
One of the aims of this short workshop is to gather experts in the area and have them share their insights of these efficient and intriguing algorithms with the audience. A focus is on PSO and other evolutionary computational algorithms that have high success rates of outperforming other types of algorithms for solving challenging optimization problems in various disciplines. Speakers will begin with introductory material, discuss some theory or heuristics behind such algorithms and provide illuminating applications of these algorithms to solve real problems. All interested to learn about these algorithms and how to use them are invited to attend this workshop free of charge.