Roy de Winter

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].

A modular optimization framework is proposed that can deal with different problem characteristics encountered in real-world design problems. The common denominator of problems considered in this work is the computationally demanding objective function used to evaluate the designs, while some of the constraint functions can be inexpensive. The framework uses radial basis functions as surrogates for the computationally expensive objectives while inexpensive functions can directly be used for searching promising solutions on the surrogates. The framework is adjustable for the number of continuous design parameters, the number of constraints, the number of solutions that can be evaluated in parallel, and different strategies can be selected on how to deal with the computationally inexpensive functions. We propose guidelines on how to set up the modular framework depending on the problem characteristics one has to deal with. To verify the working of the optimization framework, two different real-world ship design optimization problems have been optimized with different characteristics such as the number of constraints and how expensive they are. The results show that it is beneficial to directly exploit the inexpensive functions when possible and that parameterization of the problem is the most important step of the process.

Thirty years, 1993–2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. Moreover, we also discuss particle swarm optimization and differential evolution, which did not exist 30 years ago, either. One of the key arguments made in the paper is that we need fewer algorithms, not more, which, however, is the current trend through continuously claiming paradigms from nature that are suggested to be useful as new optimization algorithms. Moreover, we argue that we need proper benchmarking procedures to sort out whether a newly proposed algorithm is useful or not. We also briefly discuss automated algorithm design approaches, including configurable algorithm design frameworks, as the proposed next step toward designing optimization algorithms automatically, rather than by hand.

Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems. This paper investigates the possible benefit of a concurrent utilization of multiple simulation-based surrogate models to solve complex discrete optimization problems. To fulfill this, the so-called Self-Adaptive Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO), which features a two-stage online model management strategy, is proposed and further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal problems against several state-of-the-art non-surrogate or single surrogate assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can rapidly converge to better solutions on a majority of the test problems, which shows the feasibility and advantage of using multiple surrogate models in optimizing discrete problems.