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PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as ...
Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey
Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of ...
COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
Many real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision ...
On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms
For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences ...
A Novel Parametric benchmark generator for dynamic multimodal optimization
In most existing studies on dynamic multimodal optimization (DMMO), numerical simulations have been performed using the Moving Peaks Benchmark (MPB), which is a two-decade-old test suite that cannot simulate some critical ...