D. Parvanov, P. Tomov, T. Balabanov. Fine Tuning of LibreOffice Calc NLP Solver for Multi-Objective Optimization

Key Words: LibreOffice Calc; multi-objective optimization; NLP Solver.

Abstract. There is a common difference between single-objective optimization and multi-objective optimization. In the first case, there is only a single value as a result of the optimization. In the second case, there is a set of solutions called Pareto-optimal solutions. Single-objective solvers are giving only a single value as a result, even for multimodal functions. Because of this single-objective solver is not proper for multi-objective problems. Through additional adaptation, a single-objective solver is possible to start multiple times. Taking the results of multiple starts, the Pareto front is marked. When the solver is a metaheuristic, the front itself is difficult to achieve. With fine-tuning of the solver’s parameters, the solutions can be as close as possible.