Multi-strategy Enhanced Equilibrium Optimizer And Application Research

The Equilibrium Optimizer(EO)algorithm is a novel physics-based type of metaheuristic algorithm,which is based on the origin of the dynamic equilibrium equation in a dense container.The particles in the container represent the searched individuals and change their own particle concentration by the mass balance equation until they reach the equilibrium state,the optimal solution is found.The algorithm has received attention from domestic and foreign scholars for its simple structure,fast search speed,and high computational stability characteristics,but the algorithm is prone to fall into local optimum in the late iteration,and it is difficult to maintain the balance between exploration and exploitation capabilities,and other shortcomings.In this paper,we propose two multi-strategies enhanced EO algorithms for reactive power optimization scheduling and antenna array optimization,which are designed to improve the performance of EO and increase the application area of the algorithm.The core work of this paper is as follows:(1)The elite leading strategy and Lévy flight strategy are introduced to improve the basic equilibrium optimizer and an improved equilibrium optimizer(IEO)is proposed to effectively improve the convergence speed and development capability of the basic EO.Balancing probabilities are proposed to maintain a balance between algorithm development capability and detection capability.IEO is applied to the power system optimal reactive power dispatch(ORPD),the goal is to minimize the power loss,and tested on IEEE 57,118 and 300 bus systems,get24.76 MW,114.54 MW,373.34 MW power loss,and compared with other literature results to demonstrate that IEO is more competitive than other optimization methods.(2)Inspired by the theory of quantum computing,a quantum-encoded equilibrium optimizer algorithm(QEO)is proposed.Each individual in QEO corresponds to two positions in the search space,and the quantum encoding mechanism improves the diversity of the population.The incremental calculation of the phase angle is performed by combining the quantum revolving gate strategy with the quantum balancing pool,which effectively helps the algorithm to jump out of the local optimum and avoid premature convergence.Finally,QEO is used to minimize the side flap level(SLL)in a linear antenna array model.(3)An elite reverse learning strategy is introduced to improve the basic equilibrium optimizer,and elite reverse learning-based equilibrium optimizer(LEO)is proposed.The strategy is able to improve the exploration ability of the algorithm by searching the search space of the solution opposition,which improves the diversity of the population.And the effectiveness of the improvement is verified by the CEC2019 test function.Finally,LEO is applied to the fuel cost minimization problem,and tested on IEEE 30 bus system,obtained optimal fuel costs 800.3915 $/h.