Published: Oct 19, 2021
Choosing the proper solution within the middle of an accessible solution to resolve a controversy is thought as optimization. Optimization is more important than the rest in many applications, like engineering design and business activities. Optimization serves to reduce and maximize (for example, to attenuate costs and energy consumption, and to maximize efficiency and effectiveness). It’s no exaggeration to mention that optimization plays a crucial role practically everywhere. The most objective of optimization is to seek out a correct and uniform balance through diversification and intensification. Currently, we have very limited time, resources and money for applications in nature that don't seem to be linear and need well-informed optimization mechanisms for rigging. Therefore, we have to search out the solutions to create optimal use of those relevant resources.
Computer simulations became a necessary mechanism to unravel these optimization problems with various effective search algorithms. In fact, the sort of optimization could also be different, but the strategy of solving any complicated problem is critical to selecting the precise optimization method that's appropriate for that problem. Algorithms like particle swarm optimization algorithm (Kennedy and Eberhart, 1995), a swarm-inspired technique, and a few other nature-based algorithms, are known within the last decade to unravel complex problems, but still many important errors are used like slow, convergence, runtime management and increased complexity, etc.
Today researchers are showing that the nature-inspired optimization algorithm is more important because it is capable of solving complex problems like NP hard and other restricted and unrestricted types. Thanks to this, it became the way researchers focused on nature-inspired algorithms that are efficient at solving complex problems. Nature seems to talk and tell us something. This can be the most reason why we take lots of inspiration from nature.
According to the no free lunch theorem (Wolpert and Macready, 1997), "no particular optimization algorithm can solve all problems all told real-world domains." In contrast, an optimization is barely suitable for a few specific problems and should not be suitable for other problems. For a few years it's been the trend that some algorithms are only developed by that simulate the character / behaviours of animals / creatures / properties of insects.
This means the dearth of novelty within the development of such algorithms, and with a small change within the function / objective nature of the parameters; the researchers are attempting to check the effectiveness of the methods. However, it's good if, instead by developing such new methods (albeit redesigning the old ones); researchers concentrate on new developments that may be truly useful for all research communities. It’s quite obvious that if some algorithms are to be hybridized, the assistance of both algorithms (any parent method and optimization algorithm) will get good results. But the degree of application of this method to unravel other problems is an open challenge for all researchers within the optimization community. Researches may be focused on analyzing the performance of nature-inspired optimization on some other-specific domains in future.