Finding Multiple Alternate Solutions Using Evolutionary Multi-Objective Optimization

In many practical problem-solving tasks, instead of a single desired solution, often the goal is to find multiple alternate solutions (optimal or otherwise). In such tasks, most often, numerical methods are employed to find one solution at a time by making the desired solution the focus of the ensuing computational task. On the other hand, evolutionary multi-objective optimization (EMO) algorithms – population-based computational procedures – have demonstrated their ability to find multiple optimal solutions resulting from optimizing two or more conflicting objectives simultaneously. In this paper, we highlight the principles of an EMO procedure and discuss how it can be extended to find multiple alternate solutions for a number of different problem-solving tasks encountered in practice. This ‘multiobjectivization’ task requires researchers to choose at least two conflicting goals arising from the specific problem-solving task and apply a suitably modified version of an existing EMO algorithm to find multiple alternate solutions. These extensions broaden EMO research and its applications, and also enable a unified approach for solving various practical problem-solving tasks.