# Combining Parallel Stochastic Methods and Mixed Termination Rules in Optimization
###### Abstract
In the case of pump and cooling system management, a two-stage method based on an innovative multidimensional optimization algorithm has been proposed, which reduces overall energy consumption and discrepancies between pump and cooling system flow rates, achieving significant improvements in energy efficiency [38]. In energy network management, a coordinated scheduled optimization method has been proposed, which simultaneously considers various energy sources and uncertainty conditions. This method, using the Competitive Swarm Optimization (CSO) algorithm and adjusted based on chaos theory, has proven 50% faster than other methods and effective in managing uncertainties and energy [39]. In topological optimization, parallel processing through CPUs and GPUs has drastically improved speed and energy consumption for complex problems, achieving up to 25-times faster processing and reducing energy consumption by up to 93% [40]. Lastly, in building energy retrofitting, a multi-objective platform was used for evaluating and optimizing upgrade strategies, demonstrating that removing subsidies and providing incentives for energy upgrades yield promising results [41]. In conclusion, parallel optimization has provided significant solutions to problems related to sustainable development and enhancing sustainability. In the field of increasing the resilience of energy systems, the development of strategies for managing severe risks, such as extreme weather conditions and supplier disruptions, has been improved through parallel optimization, enabling safer and more cost-effective energy system operations [42]. In addressing groundwater contamination, the use of parallel optimization has highlighted the potential for faster and more effective design of remediation methods, significantly reducing computational budgets and ensuring better results compared to other methods [43]. Finally, in the field of sustainable production, metaheuristic optimization algorithms have proven useful for improving production scheduling, promoting resource efficiency, and reducing environmental impacts, while contributing to the achievement of sustainable development goals [44]. By harnessing multiple computational resources, parallel optimization allows for the simultaneous execution of multiple algorithms, leading to faster convergence and improved performance. These computational resources can communicate with each other to exchange information and synchronize processes, thereby contributing to faster convergence towards common solutions. Additionally, leveraging multiple resources enables more effective handling of exceptions and errors, while increased computational power for conducting more trials or utilizing more complex models leads to enhanced performance [45]. Of course, this process requires the development of suitable algorithms and techniques to effectively manage and exploit available resources. Each parallel optimization algorithm requires a coherent strategy for workload distribution among the available resources, as well as an efficient method for collecting and evaluating results. Various researchers have developed parallel techniques, such as the development and evaluation of five different parallel Simulated Annealing (SA) algorithms for solving global optimization problems. The paper compares the performance of these algorithms across an extensive set of tests, focusing on various synchronization and information exchange approaches, and highlights their particularly noteworthy performance on functions with large search spaces where other methods have failed [46]. Additionally, other research focuses on the parallel application of the Particle Swarm Optimization (PSO) method, analyzing the performance of the parallel PSO algorithm on two categories of problems: analytical problems with low computational costs and industrial problems with high computational costs [47].