Transmission network expansion planning using a modified artificial bee colony algorithm

Transmission network expansion planning (TNEP) problem is an essential part of power system expansion planning, and it is an extremely complex nonlinear, nonconvex, mixed-integer optimization problem. Solution to such a computationally intensive problem is a challenge for any optimization algorithm. Consideration of security constraints makes the problem even more formidable. Although various conventional and metaheuristic methods have been used in the past to solve such problem, scope for better optimization techniques always remain. The artificial bee colony (ABC) algorithm is one of the newest swarm intelligence-based optimization algorithms, which has delivered promising results in solving numerical optimization problems. However, the algorithm is quite less efficient in solving real-life constrained engineering problems. In this paper, a modified ABC (MABC) algorithm is formulated by incorporating the idea of global attraction, universal gravitation, and by introducing modified ways of searching in various bees' phases of the ABC algorithm. The MABC is able to get better results in a very efficient manner, when used for solving various benchmark functions. The efficiency and effectiveness of the MABC algorithm in solving constrained engineering problems is demonstrated by solving TNEP problems for different systems. The proposed method is tested on IEEE 24 bus system, South Brazilian 46 bus system, Colombian 93 bus system for direct current TNEP model, and Garver 6 bus system for alternating current TNEP model. Results confirm that MABC can be an attractive alternative to the existing optimization algorithms for solving very complex nonlinear engineering optimization problems in a real-world situation.