Evolutionary Computation & Swarm Intelligence

Dear Colleagues,

The vast majority of real-world problems can be expressed as an optimisation task by formulating an objective function, also known as cost or fitness function. The most logical methods to optimise such a function when (1) an analytical expression is not available; (2) mathematical hypotheses do not hold; and (3) the dimensionality of the problem or stringent real-time requirements make it infeasible to find an exact solution mathematically; are from the field of Evolutionary Computation (EC) and Swarm Intelligence (SI). The latter are broad and still growing subjects in Computer Science that studies metaheuristic approaches, i.e., those approaches which do not make any assumption on the problem to function, inspired from natural phenomena as, in the first place, the evolution process and the collaborative behaviours of groups of animals and communities respectively. State-of-the-art EC and SI frameworks are the genetic algorithm (GA), the evolution strategy (ES), differential evolution (DE), particle swarm optimisation (PSO), bacterial foraging optimisation (BFO), ant colony optimisation (ACO) and the memetic algorithm (MA). The literature is abundant with several other approaches which share a common goal: to find a suboptimal solution of satisfactory quality by alternating explorative searches to exploitative ones.

These optimisation approaches have shown to be flexible, robust, and are thus often referred to as general-purpose or black-box optimisation algorithms. Their success in dealing with several complex problems, e.g., in engineering, economy, or telecommunication, have made them very popular, and they are widely used by practitioners.

However, it is clear—from the No Free Lunch Theorem (NFLT)—that to achieve high-quality solutions, general-purpose methods either require a thorough fine tuning or some sort of self-adaptive capability to become more specialised in the problem at hand. Furthermore, the also state-of-the-art optimisation framework carries flaws for which an easy fix is yet to come (e.g., premature convergence, stagnation, structural bias, lack of self-adaptivity to the specific problem, curse of dimensionality).

In this light, we invite the research community to submit their original contribution to address several challenging aspects in EC and SI, and we welcome studies investigating metaheuristics for optimisation from both the theoretical and the applied side, regardless of the nature of the problem (single-objective, multiobjective, dynamic, etc.). Authors are encouraged to submit their formal and technically sound manuscripts to cover (but not be limited to) the following aspects:

- Algorithmic behaviour and performances;
- Tailored algorithmic design for real-world applications;
- Advances in memetic computing;
- Memory-saving and compact optimisation;
- Real-time optimisation;
- Large-scale optimisation.

Dr. Fabio Caraffini
Prof. Dr. Alfredo Milani
Dr. Valentino Santucci
Guest Editor

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• Algorithmic design
• Large-scale optimisation
• Compact optimisation
• Estimation of distribution algorithms
• Structural bias
• Parameters tuning
• Stagnation
• Premature convergence
• Stagnation
• Benchmarking
• Evolution strategies
• Genetic algorithm
• Genetic programming
• Evolutionary programming
• Memetic algorithms
• Memetic computing
• Hyperheuristics
• Differential evolution
• Particle swarm optimisation
• Ant colony optimisation
• Bacterial foraging optimisation