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In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the...
Bare-bones Particle Swarm Optimization (BPSO) is a simplified PSO variant, which has shown potential performance on many multimodal optimization problems. However, BPSO is also possible to be trapped into local optima for high-dimensional and complicated optimization problems. In order to enhance the performance of BPSO, this paper presents a modified BPSO, called NMBPSO. It combined the ideas of...
This paper proposed a novel bio-inspired optimizer, namely the root system growth algorithm (RSGA), which adopts the root foraging, memory and communication, and auxin-regulated mechanisms of the root system. When tested against benchmark functions, the RSGA markedly outperforms the CMA-ES, PSO, GA, and DE algorithms in terms of accuracy, robustness and convergence speed.
Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC algorithm, which is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. For these insufficiencies,...
This paper presents a modified fruit fly optimization algorithm(FOA). The proposed modified FOA establishes a balanced tradeoff between exploration and exploitation, and thus overcomes original FOA's drawbacks of premature convergence and easy trapping in a local optima. In the proposed modified FOA, firstly, the whole population performs a global search; Secondly, the whole population are sequenced...
Particle Swarm Optimization has been widely used to solve real world problems, mainly when there are too many variables to be optimized and these variables are continuous. In nature one can observe many examples of cooperative behaviors that lead to complex problem solving. Recently, some Particle Swarm Optimization variations gracefully incorporate such cooperative features with consequent beneficial...
In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he called the bacterial foraging optimization algorithm (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. This...
This paper introduces a novel particle swarm optimization algorithm based on the concept of black holes in physics, called random black hole particle swarm optimization (RBH-PSO) for the first time. In each dimension of a particle, we randomly generate a black hole located nearest to the best particle of the swarm in current generation and then randomly pull particles of the swarm into the black hole...
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