This paper proposes a binary particle swarm optimization (BPSO) with time-varying acceleration coefficients (TVAC) for solving optimal placement of wind turbines within a wind farm. The objective is to extract the maximum turbine power output in a minimum investment cost within a wind farm. The BPSO-TVAC algorithm is applied to 100 square cells test site considering uniform wind and non-uniform wind speed with variable direction characteristics. Linear wake model is used to calculate downstream wind speed. Test results indicate that BPSO-TVAC investment cost per installed power of both uniform and non-uniform wind speed with variable wind direction are lower than those obtained from genetic algorithm and evolutive algorithm, BPSO-TVIW (time-varying inertia weight factor), BPSO-RANDIW (random inertia weight factor) and BPSO-RTVIWAC (random time-varying inertia weight and acceleration coefficients), leading to maximum power extracted in a least investment cost manner. (C) 2012 Elsevier Ltd. All rights reserved.