Swarm Intelligence and Bio-Inspired Computation

HIASA: Hybrid Improved Artificial Bee Colony and Simulated Annealing based Attack Detection Algorithm in Mobile Ad-hoc Networks (MANETs)

“…Swarm Intelligence (SI) is the collective conduct of self structured systems, decentralized, natural or non-natural [12][13]. The motivation frequently originates from nature particularly biological systems.…”

Mineração em Grandes Massas de Dados Utilizando Hadoop MapReduce e Algoritmos Bio-inspirados: Uma Revisão Sistemática

“…Nesse caso, a diversificação pode beneficiar a busca da melhor solução. Soluções determinís-ticas tendem a falhar quando aplicadas em problemas que o espaço de busca é muito grande e tendem a aumentar consideravelmente [10] [54]. Mineração de dados em Big Data pode ser visto como um problema de otimização onde busca-se encontrar a melhor solução, como os valores dos centróides de uma tarefa de agrupamento, em um determinado tempo limite.…”

Quantum Artificial Bee Colony Algorithm for Numerical Function Optimization

“…Some of these algorithms gained much popularity because of their versatility, flexibility and powerful adaptive search technique and efficient in solving practical nonlinear problems. There are two noteworthy features of swarm based algorithms are decentralized control and self-organization that lead to a distinguished behavior [1]. This behavior is a property that appears through interactions among system component, and it is impossible to be achieved by any of the components of the system acting alone.…”

“…Despite the popularity and success of bio-inspired computing, there remain many challenging issues such as gaps between theory and practice, classifications, parameter tuning, lack of truly large-scale real-world applications and the selection of the appropriate algorithm for specific problem [1]. This paper focuses on the ABC algorithm which is a population-based meta-heuristic approach originally proposed by D. Karaboga [4].…”

“…The convergence is also an issue in some cases. The exploration is defined as the ability to independently seeking for the global optimum, while the exploitation is defined as the ability to apply the existing knowledge to look for better solutions [1].…”