# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 1 Introduction  
Given the continuous increase of electricity demand and unbalanced demand distribution, proper adjustment of the unit status in order to realize rational use of the generation power resource is of significant importance to the power system operators [1]. The normal method to solve the UC problem is to minimize the economic cost and pollutant emissions by arranging to start-up or shut-down the units while meeting various practical constraints including power demand balance, generation limit, etc. [2]. Due to the remarkable complexity, constraints and binary switching effect of the system [3], UC is widely regarded as a nonlinear, multi-constraint, large-scale and mixed-integer problem. Many optimization algorithms have attempted to solve the UC problem. Some traditional mathematical methods, such as the dynamic programming [4], integer programming [5], mixed-integer programming [6], branch and bound methods [7], evolutionary programming [8] and Lagrangian relaxation methods [9; 10] have been employed. Apparent advantages and defects can be found with these algorithms. For example, the Lagrangian relaxation methods is suitable for solving the large-scale constrained problem; however, the algorithm may oscillate during the iteration and the convergence speed is slow. Dynamic programming method is suitable for solving the problems of multi-stage decision process optimization, whereas it is easy to cause dimensional disaster in high-dimensional applications. With development of the meta-heuristic (MA) optimization algorithms, many scholars have applied the evolutionary based MA approaches to solve the UC problem. Generally speaking, MA approaches construct algorithmic logic based on common phenomena in nature and life, and maintain a single or a population of available solutions to search for the optimal solution for complex problems. Featured MA tools have been adopted in solving UC problem include genetic algorithms (GAs) [11; 12], simulated annealing (SA) [13], particle swarm optimization (PSO) [14] and ACO [15], as well as teaching-learning based optimization (TLBO) [16], etc. Some popular PSO variants such as binary particle swarm optimization (BPSO) [17; 18], symmetric binary particle swarm optimization [19], quantum-inspired particle swarm optimization (QPSO) [20], hybrid particle swarm optimization (HPSO) [21; 22] etc., have also been used. Additionally, other variants of PSO algorithm have been applied in some engineering application, for example, the Bluetooth energy management and model updating [23; 24]. Inspired by the PSO algorithm and to further increase the refinement ability, [PERSON] and [PERSON] proposed the competitive swarm optimizer (CSO) [25] in 2015. The CSO adopts a novel learning mechanism without the use of \(g_{best}\) and \(P_{best}\). It demonstrates competitions between particle pairs, and the losers should update their velocity and position by learning from the winner. Although the principle of CSO is fairly simple, the performance of convergence speed and optimum result is considerably improved compared to its ancestor PSO, and this competitive mechanism has effectively solved the large-scale optimization problem with high dimension and complexity. CSO and its variants have been used to optimal installation of multiple distributed generation (DG) units [26], economic dispatch [27; 28] and other large-scale power system problems [29]. In this paper, a novel BCSO algorithm is proposed for solving the UC problem. The major contributions of the paper are shown as below:  
1. To solve the problem of binary switching in the engineering application, a novel binary competitive swarm optimization (BCSO) algorithm has been proposed based the framework of CSO algorithm. 2. The proposed BCSO algorithm was applied to solve the UC problem with different unit number, which provided a new method for solving large-scale optimization problems. 3.