Neural Computing and Applications
In this paper, shunt capacitors are effectively placed in two radial distribution networks with 69 and 85 nodes for the purpose of reducing the sum of capacitor investment cost and energy loss cost by using a novel metaheuristic, called slime mould optimization algorithm (SMOA). The main duty of SMOA is to find the most suitable position of the shunt capacitors and to determine optimal generation of the shunt capacitors over a year with three load levels. In addition to comparison with previous methods in the literature, SMOA is also compared to two other applied methods including bonobo optimization algorithm (BOA) and tunicate swarm algorithm (TSA). The novelty of the paper is to apply three new methods in which SMOA and TSA were developed in early 2020 and BOA was introduced in 2019. The three methods can reach the same success rate of 100%, but SMOA is more powerful. In fact, SMOA can reach better minimum, mean and maximum total costs, faster convergence speed and more effective stability of fifty independent runs. BOA and TSA cannot find one the same good solution as SMOA even they are run 50 times for each study case. Comparison with previous methods in the literature indicates that SMOA can find better position and more suitable generation for shunt capacitors and it can get less total cost, and use smaller population size and a lower number of iterations. The best result from SMOA is also the main contribution of the study and it is recommended that SMOA should be used for placing capacitors in radial distribution networks.