Developed by Dr. Russell Eberhart and Dr. James Kennedy in 1995, Particle Swarm Optimisation is an Artificial Intelligence technique based on the study of collective behavior in decentralized, self-organized systems.
Swarm Intelligence is an example of a "behavior-based" system, as it relies on low-level agents with minimum competency that are placed in a specific environment and have a limited time to act. Each agent acts autonomously, without central control, and the problem-solving nature of the swarm comes from its emergent behavior. By mimicking the social behavior of bees "swarming" or birds flocking, this technique represents possible solutions as "particles" that "swarm" through the search space. Over time, the particles accelerate towards the "leader," or the best solution. The main advantage of Particle Swarm Optimisation over other global optimisation techniques is the large number of particles that make up the swarm. This makes the technique notably resilient to the problem of local optima.
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Mr Benno Giuliani |
“We selected SolveIT Software for Integrated Planning of the NCA demand chain as they are experienced in optimising complex and dynamic logistics processes. The NCA coal chain is a key asset in the Xstrata Coal Australia portfolio and is set for significant expansion in coming years. Ensuring we exceed customer expectations through optimisation of value, quality, quantity and cost of XCQ’s coal products is our primary goal, and logistics optimisation initiatives across our systems landscape is a key driver for improved commercial and operational outcomes.”
Transport and Logistics Manager
Xstrata Coal
Xstrata Coal