Optimization and decision making problems in various fields of engineering have a major impact in this current era. Processing time and utilizing memory is very high for the currently available data. This is due to its size and the need for scaling from zettabyte to yottabyte. Some problems need to find solutions and there are other types of issues that need to improve their current best solution. Modelling and implementing a new heuristic algorithm may be time consuming but has some strong primary motivation - like a minimal improvement in the solution itself can reduce the computational cost. The solution thus obtained was better. In both these situations, designing heuristics and meta-heuristics algorithm has proved it’s worth. Hyper heuristic solutions will be needed to compute solutions in a much better time and space complexities. It creates a solution by combining heuristics to generate automated search space from which generalized solutions can be tuned out. This paper provides in-depth knowledge on nature-inspired computing models, meta-heuristic models, hybrid meta heuristic models and hyper heuristic model. This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain. Various traditional algorithms and new generation meta heuristic algorithms has also been explained for giving readers a better understanding.

在当前的这个时代，工程各个领域的优化和决策问题都产生了重大影响。对于当前可用的数据，处理时间和占用内存的时间非常高。这是由于它的大小以及从Zettabyte到yottabyte缩放的需要。一些问题需要找到解决方案，而其他类型的问题则需要改进其当前最佳解决方案。对新的启发式算法进行建模和实现可能会很耗时，但是具有一些强大的主要动机-如解决方案本身的最小改进可以降低计算成本。由此获得的解决方案更好。在这两种情况下，设计启发式算法和元启发式算法已证明是值得的。需要超级启发式解决方案来以更好的时间和空间复杂性来计算解决方案。它通过结合启发式方法来生成解决方案，以生成自动搜索空间，从中可以调出广义解决方案。本文提供了有关自然启发式计算模型，元启发式模型，混合元启发式模型和超启发式模型的深入知识。这项工作的主要贡献在于，通过针对任何一般问题领域的元启发式算法构建超启发式方法。还解释了各种传统算法和新一代元启发式算法，以使读者更好地理解。这项工作的主要贡献在于，通过针对任何一般问题领域的元启发式算法构建超启发式方法。还解释了各种传统算法和新一代元启发式算法，以使读者更好地理解。这项工作的主要贡献在于，通过针对任何一般问题领域的元启发式算法构建超启发式方法。还解释了各种传统算法和新一代元启发式算法，以使读者更好地理解。