ABSTRACT
Real-world design problems found in the engineering domain are usually multiobjective problems, that is, there is more than one objective that needs to be accomplished at the same time. The trouble lies in the fact that in most of the cases, these objectives go against one another. The solution in these cases is to come to a point where a perfect/optimal balance could be struck between these objectives. In a way, one objective needs to be traded off to get to some part of another and that is where the devil lies. Another option that researchers and problem-solvers go for is approximations and consequent ranking of possible solutions. This is done so that the best ranked solution could be adopted for a given application. This will also help the decision-makers to take smart and proactive decisions given in a scenario. As can be guessed very easily, multiobjective problems have additional challenges such as an increased time complexity, in homogeneity and high dimensionality. To add to this, there is no guarantee that the obtained solution or the chosen rank system will actually help the cause. The Pareto front curves usually form higher order plots such as a surface and so on and this makes the situation even more challenging. A number of nature-inspired optimization algorithms have surfaced in the recent past and their performance has been well documented clearly representing the areas of their utility, their pros as well as cons. Such algorithms include genetic algorithms (GAs) [1], particle swarm optimization (PSO), cuckoo search (CS), and so on, as discussed in the previous chapters. In Sections 16.2 through 16.5, we will be talking about a new algorithm called ower pollination algorithm, a nature-inspired algorithm motivated by how pollination takes place in owers. We will also be discussing its variants, hybridization with other contemporary algorithms, scope of application as well as applications.
