Optimization of Chiller Loading Problem Using Improved Golden Jackal Optimization Algorithm Leads to Reduction in Energy Consumption

This paper proposes a modified golden jackal optimization(IGJO)algorithm to solve the OCL(which stands for optimal cooling load)problem to minimize energy consumption.In this algorithm,many tools have been developed,such as numerical visualization,local field method,competitive selectionmethod,and iterative strategy.The IGJO algorithm is used to improve the research capabilities of the algorithm in terms of global tuning and rotation speed.In order to fully utilize the effectiveness of the proposed algorithm,three famous examples of OCL problems in basic ventilation systems were studied and compared with some previously published works.The results show that the IGJO algorithm can find solutions equal to or better than other methods.Underpinning these studies is the need to reduce energy consumption in air conditioning systems,which is a critical business and environmental decision.The Optimal Chiller Load(OCL)problem is well-known in the industry.It is the best method of operation for the refrigeration plant to satisfy the requirement of cooling.In order to solve the OCL problem,an improved Golden Jackal optimization algorithm(IGJO)was proposed.The IGJO algorithm consists of a number of parts to improve the global optimization and rotation speed.These studies are intended to address more effectively the issue of OCL,which results in energy savings in air-conditioning systems.The performance of the proposed IGJO algorithm is evaluated,and the results are compared with the results of three known OCL problems in the ventilation system.The results indicate that the IGJO method has the same or better optimization ability as other methods and can improve the energy efficiency of the system’s cold air.

Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and costs using improved ABC algorithm

The aim of this paper is simultaneous mini- mization of hydrothermal units to reach the best solution by employing an improved artificial bee colony （ABC） algorithm in a multi-objective function consisting of economic dispatch （ED） considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve con- sidering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetfc algorithm （GA）, the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.