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Particle swarm optimisation (PSO) is a bio-inspired swarm based approach to solving optimisation problems. The algorithm functions as a result of particles traversing and evaluating the problem space, eventually converging on the optimum solution. This paper applies a number of PSO variants to the dynamic economic emission dispatch (DEED) problem. The DEED problem is a multi-objective optimisation...
Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent learns to improve its performance in an environment by maximising a reward signal. In multi-objective Reinforcement Learning (MORL) the reward signal is a vector, where each component represents the performance on a different objective. Reward shaping is a well-established family of techniques that...
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