Modeling a mixed-integer-binary small-population evolutionary particle swarm algorithm for solving the optimal power flow problem in electric power systems

Optimal Power Flow Using the Jaya Algorithm

“…Remarkably, the average computational time of one iteration for this case is 1.83 s. The optimal adjustments of control variables and optimal values of cost minimization are tabulated in Table 6. For further validation, the results obtained with Jaya algorithm are compared with those of TLBO [28], GSA [34], BBO [34], PSO [35], DE [36], and GWO [36], as shown in Table 7. Jaya algorithm obviously obtained a more superior solution.…”

“…Teaching-Learning-Based Optimization (TLBO) [28] 129,682.844 NA Gravitational Search Algorithm (GSA) [34] 129,565 76.19 Biogeography-Based Optimisation (BBO) [34] 129,686 78.14 Particle Swarm Optimization(PSO) [35] 130,288.21 NA Differential Evolution (DE) [36] 129,582 79.41 Grey Wolf Optimizer (GWO) [36] 129,720 79.58…”

Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms

“…The optimal control variables and optimal data are introduced in Table 3. Figure 4 points out Pareto optimal solutions and obtained best comDownloaded by [New York University] MatPower [36] 129,660 PSO [11] 130,288 GSA [19] 129,565 TLO [23] 129,682 Proposed DE 129,582 Proposed GWO 129,720 Table 3. Figure 5 shows three-dimensional Pareto optimal solutions and achieved best compromise solution.…”

“…However, these methods may be stuck in a local minimum that prevents the algorithm for producing the real optimal solution. To tackle this shortage, several artificial intelligence based techniques were applied to solve the OPF problem such as tabu search [6], genetic algorithm (GA) [7][8][9], particle swarm optimization (PSO) [10,11], biogeographybased optimization [12,13], artificial bee colony (ABC) [14], harmony search algorithm (HSA) [15], and many more [16][17][18][19][20][21][22][23]. These algorithms may solve a single objective function or a multi-objective function based on the problem formulation.…”

Micro-differential evolution: Diversity enhancement and a comparative study

“…Simulations are conducted on only four benchmark functions, where competitive performance is reported. A mixed-integer-binary small-population PSO is proposed in [58] for solving a problem of optimal power flow. The constraint handling technique used in this algorithm is based on a strategy to generate and keep its four decision variables in feasible space through heuristic operators.…”