The ant colony optimization (ACO) is the most exceptionally fundamental swarm-based
solver for realizing discrete problems. In order to make it also suitable for solving continuous …

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm
optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish …

Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos
bioinspirados baseados no comportamento de populações para o desenvolvimento de …

Two novel extensions for the well known ant colony optimization (ACO) framework are
introduced here, which allow the solution of mixed integer nonlinear programs (MINLPs) …

Research into ant colony algorithms for solving continuous optimization problems forms one
of the most significant and promising areas in swarm computation. Although traditional ant …

The successful handling of numerous real–world complex problems has increased the
popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful …

In this paper, we proposed a hybrid system to predict corporate bankruptcy. The whole
procedure consists of the following four stages: first, sequential forward selection was used …

We propose a novel hybrid algorithm named ACO–FA, which integrate ant colony
optimization (ACO) with firefly algorithm (FA) to solve unconstrained optimization problems …

Multi-agent reinforcement learning (MARL) is a widely researched technique for
decentralised control in complex large-scale autonomous systems. Such systems often …

This paper presents a method that allows two wheeled, mobile robots to navigate unknown
environments while cooperatively carrying an object. In the navigation method, a leader …