Hot off the Press: Parallel Multi-Objective Optimization for Expensive and Inexpensive Objectives and Constraints

Roy de Winter, Bas Milatz, Julian Blank, Niki van Stein, Thomas Bäck, Kalyanmoy Deb

Abstract: This paper presents the Inexpensive Objectives and Constraints Self-Adapting Multi-Objective Constraint Optimization algorithm using Radial Basis Function Approximations (IOC-SAMO-COBRA). This algorithm is introduced to address constraint multi-objective optimization problems that involve a mix of computationally expensive and inexpensive evaluation functions. The IOC-SAMO-COBRA algorithm iteratively learns the expensive functions with radial basis function surrogates, while the inexpensive functions are directly used in the search for promising solutions. Two benefits of using the inexpensive functions directly include: (1) The inexpensive functions do not introduce approximation errors like surrogates do. (2) Time can be saved as there is no need to fit and interpolate surrogate models for the inexpensive functions. Results on 22 test functions indicate that exploiting the inexpensive functions can be beneficial as this results in better Pareto front approximations compared to using surrogates for both expensive and inexpensive functions.This paper serves as an extended abstract for the Hot-off-the-Press track at GECCO 2024, building upon the paper Parallel Multi-Objective Optimization for Expensive and Inexpensive Objectives and Constraints by de Winter et al, published in Swarm and Evolutionary Computation, Volume 86 (2024) [4].