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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.
An improved QPSO algorithm based on entire search history (ESH-QPSO) is proposed. ESH-QPSO is an integration of the entire search history scheme and a standard quantum-behaved particle swarm optimization (QPSO). It guarantees that all updated positions are not revisited before, which helps prevent premature convergence. The entire search history scheme partitions the continuous search space into sub-regions...
Particle swarm optimization can be viewed as a system with two populations: a population of current positions and a population of personal best attractors. In genetic algorithms, crossover is applied after selection - the goal is to create a new offspring solution using components from the best available solutions. In a particle swarm, the best available solutions are in the population of personal...
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