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CHAPTER 3: METHODOLOGY 3.1 Quantum Robotic Darwinian particle Swarm Optimization (QRDPSO) Following the many examples of behavior based biological collective architectures presented in the literature, researchers have been proposing ever-improving designs of novel swarm robotic algorithms. This area of research, belonging to swarm intelligence, studies large collections of relatively simple agents that can collectively solve problems that are far too complex for a single agent, or that can display the robustness and adaptability to environmental variation displayed by biological agents (Beni, 2004). This chapter presents the core of this Thesis by methodically describing the Quantum Robotic Darwinian Particle Swarm Optimization (QRDPSO). It is, however, noteworthy that, although all subsequent chapters revolve around the QRDPSO algorithm herein proposed, their methods, tools and insights can, and should, be applied to other swarm robotic algorithms. This thesis divided into three main contributions: i. A novel QRDPSO algorithm that improves convergence speed rate during swarm-robot exploration over RDPSO algorithm. ii. A coordinated swarm movement strategy which conserve robot’s energy and extend robot’s lifetime during exploration iii. The MR-LEACH schema that the robots more flexible movement and increase lifetime over benchmark protocol such as MANET in avoiding local optima and finding global best (victims).
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