Evolutionary Computation & Swarm Intelligence

When a problem is too complex, time-sensitive, or poorly suited to exact mathematical methods, how can a useful solution still be found? Evolutionary Computation & Swarm Intelligence brings together ten research articles on nature-inspired optimisation methods and the challenges of applying them to real-world tasks.

Edited by Fabio Caraffini, Valentino Santucci, and Alfredo Milani, this collection is a research-focused reprint from the journal Mathematics. Its contributions range from algorithm design and benchmarking to applications in data analysis, robotics, and large-scale optimisation.

Research across methods and applications

The articles consider different ways to search complex solution spaces, including evolutionary approaches and swarm-based methods. Across the collection, readers encounter work on dynamic data-stream clustering, classification with imbalanced data, robot analysis, genetic algorithms, and particle-swarm optimisation.

From algorithm design to evaluation

Finding a promising optimisation method is only part of the problem. The volume also addresses how algorithms are designed, tuned, and benchmarked, including concerns such as premature convergence and the generation of infeasible solutions. This makes the collection useful for readers interested not just in proposed methods, but in how their behavior and performance are examined.

Applied problems in view

Other contributions explore wireless-sensor-network routing, large-scale optimisation, data preprocessing, approximate file matching, and multi-objective search. The range of topics shows how closely algorithmic choices are tied to the shape of the problem being solved.

A collection for computational intelligence readers

Because this is an edited volume of separate research papers rather than a step-by-step textbook, it suits readers who want to explore specific studies and applications in evolutionary computation and swarm intelligence. Researchers, students, and practitioners working with metaheuristics or optimisation problems can use its varied contributions to follow current approaches across several application areas.

For a focused look at how nature-inspired optimisation is developed, tested, and applied, this collection offers ten distinct research perspectives in one volume.