optimal Power Flow (OPF) is one of the most vital tools for power system operation analysis, which requires a complex mathematical formulation to find the best solution.
Conventional methods such as Linear Programming, NewtonRaphson and Non-linear Programming were previously offered to tackle the complexity of the OPF. However, with the emergence of artificial intelligence, many novel techniques such as artificial intelligence and swarm intelligence approaches have also received great attention. This paper described the use of Cultural-based Bee Colony to solve the OPF problems. The results show that solving the OPF problem by the Cultural based Bee Colony are more effective than other swarm intelligence methods in the literature.
Manufacturing process problems in industrial systems are currently large and complicated. The effective methods for solving these problems using a finite sequence of instructions can be classified into two groups; optimization and meta-heuristic algorithms. In this paper, a well-known meta-heuristic approach called Firefly Algorithm was used to compare with Shuffled Frog-leaping Algorithm. All algorithms were implemented and analyzed with manufacturing process problems under different conditions, which consist of single, multi-peak and curved ridge optimization. The results from both methods revealed that Firefly Algorithm seemed to be better in terms of the mean and variance of process yields including design points to achieve the final solution.
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