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Economic early warning is the recognition and judgment of the state of economic operation, and its research results directly affect the rational formulation of macro-control policies. However, the traditional early warning methods are mainly based on expert experience or simple statistical model, which are difficult to reflect the nature of highly nonlinear economic system and can not meet the objective requirements of macroeconomic early warning. Based on the above background, the purpose of this study is to design an economic early warning system based on improved genetic and BP hybrid algorithm and neural network. Based on the overview of macroeconomic early warning at home and abroad, this study expounds the design of early warning index system, early warning model, establishment of early warning system and other issues in the macroeconomic early warning theoretical system; deeply analyses the theoretical methods of BP neural network and adaptive mutation genetic algorithm, and discusses the feasibility of realizing macroeconomic early warning by BP neural network and adaptive mutation genetic algorithm, The improved genetic and BP hybrid algorithm and neural network economic early warning model are established. Finally, the experimental results show that the correlation coefficient between the composite index and the comprehensive early warning is 0.89, and the delay number is 0, which shows that the early warning index obtained by the early warning system can accurately reflect the actual economic fluctuations. The results show that the improved genetic and BP hybrid algorithm and neural network economic early warning system are effective, feasible and have good accuracy. [ABSTRACT FROM AUTHOR]

• Multi-objective CCHP problem is modelled by economic, energy & environmental criteria. • Application of ESS modeling based different practical and nonlinear constraints. • Application of developed MOGWA based on Pareto criterion and fuzzy theory. • Providing effective method for planning of the CCHP, TES & operations based DSR. Increasing energy demand, energy generation costs, and environmental concerns are among the reasons driving governments to develop technologies in order to save energy and reduce greenhouse gas emissions. The combined cooling, heating, and power systems are among the technologies developed to reduce primary energy consumption, costs, and greenhouse gas emissions. Combined cooling, heating, and power systems generate power on-site by a primary actuator. The difference between these systems and traditional power plants is that these systems use the heat dissipated from the primary actuator to meet consumers' heating and cooling demands. These systems convert the waste heat in the power generation sector into the heating and cooling energy required by users to improve performance. This study aims to develop a multi-objective model considering three objective functions in energy, economic, and environmental areas as well as different practical and nonlinear constraints to effectively implement this system in the power grid. Given that these functions are inherently contradictory, an optimized multi-objective grey wolf algorithm is proposed as a solution. The proposed multi-objective algorithm is a model developed based on non-dominated sorting theory, variable detection, memory-based strategy selection, and fuzzy theory to select the optimal Pareto from the set of solutions, which has powerful performance in solving the above problem. The results obtained from the figures and tables in different time periods indicated the proposed method increased by about 2% in summer and 2% in winter compared to other methods. In addition, the computation time for energy system planning was acceptable. [ABSTRACT FROM AUTHOR]