Preface

Advanced analytics in both science and technology is growing rapidly, and optimization is essential for this growth. Applying innovative optimization approaches such as population-based metaheuristic models instead of using traditional models can help researchers obtain more benefits. In many fields and different domains of human activity, optimization problems are encountered frequently. As a result, we must find optimal or near-optimal solutions for specific issues to meet certain constraints. More specifically, optimization is concerned with the development of efficient and reliable computing infrastructures, which will be used, among other things, to accelerate meta-heuristic techniques by significantly improving their performance. Numerous heuristic algorithms have been developed to find faster, near-optimal solutions to reduce time to market.

Moreover, heuristic algorithms can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics, including genetic algorithms, Simulated Annealing, and Gray Wolf Optimization. This book provides an overview of ACO applications in various fields as well as their technical details.
