Ant colony optimization theory: a survey
Theoretical Computer Science
Integral Particle Swarm Optimization with Dispersed Accelerator Information
Fundamenta Informaticae - Swarm Intelligence
Tackling magnetoencephalography with particle swarm optimization
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Artificial physics optimisation: a brief survey
International Journal of Bio-Inspired Computation
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Light responsive curve selection for photosynthesis operator of APOA
International Journal of Bio-Inspired Computation
International Journal of Computer Applications in Technology
Using APPM-trained ANN to solve stochastic expected value mode
International Journal of Bio-Inspired Computation
Mathematical methods to quantify and characterise the primary elements of trophic systems
International Journal of Computer Applications in Technology
Using hybrid APPM to solve Lennard-Jones cluster problems
International Journal of Wireless and Mobile Computing
Automatic semantic annotation by using fuzzy theory for natural images
International Journal of Wireless and Mobile Computing
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In this paper, a new stochastic optimisation algorithm is introduced to simulate the plant growing process. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomena. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction. In our algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Phototropism operator is designed to introduce the fitness function value, as well as to decide the growing direction. To test the performance, it is used to solve the directing orbits of chaotic systems, simulation results show this new algorithm increases the performance significantly when compared with other four optimisation algorithms.