Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization

Nature-inspired metaheuristics of the swarm intelligence field are a powerful approach to solve electromagnetic optimization problems. Ant lion optimizer (ALO) is a nature-inspired stochastic metaheuristic that mimics the hunting behavior of ant lions using steps of random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. To extend the classical single-objective ALO, this paper proposes four multiobjective ALO (MOALO) approaches using crowding distance, dominance concept for selecting the elite, and tournament selection mechanism with different schemes to select the leader. Numerical results from a multiobjective constrained brushless direct current (DC) motor design problem show that some MOALO approaches present promising performance in terms of Pareto-optimal solutions.

Evaluation of Nature Inspired Metaheuristics for Search and Reconnaissance Operations by Rotary-wing Aircrafts

The main objective of this work is the evaluation of two nature inspired meta-heuristics, Genetic Algorithms and Ant Colony, for the development of an application that can generate optimized routes for aircraft, attending the requirements of the Brazilian Navy. This work presents the methods developed, complying with two main constraints: checkpoints mobility and limited aircraft autonomy. It also presents the results of tests performed with the methods developed and an evaluation of their performances.

Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms

In recent years, a plethora of new metaheuristic algorithms have explored different sources of inspiration within the biological and natural worlds. This nature-inspired approach to algorithm design has been widely criticised. A notable issue is the tendency for authors to use terminology that is derived from the domain of inspiration, rather than the broader domains of metaheuristics and optimisation. This makes it difficult to both comprehend how these algorithms work and understand their relationships to other metaheuristics. This paper attempts to address this issue, at least to some extent, by providing accessible descriptions of the most cited nature-inspired algorithms published in the last 20 years. It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation, and finishes with a discussion of future directions for the field.