While Bayesian optimization (BO) has achieved great success in optimizing expensive-to-evaluate black-box functions, especially tuning hyperparameters of neural networks, methods such as random search (Li et al., 2016) and multifidelity BO (e.g. Klein et al. (2017)) that exploit cheap approximations, e.g. training on a smaller training data or with fewer iterations, can outperform standard BO a...

Abstract Chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to more flexible complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp (EChOA) improve accuracy solutions. First, highly disruptive polynomial mutation ...

Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called ‘‘var...

ABSTRACT: The paper presents the discrete dimension optimization of unbraced rigid steel plane frames. The optimization of steel frames was carried out by the Mixed-Integer Non-linear Programming (MINLP) approach. The MINLP is a combined discrete-continuous optimization technique. It performs the discrete optimization of discrete decisions simultaneously with the continuous optimization of cont...

‎This paper describes a new optimization method for solving continuous semi-infinite linear problems‎. ‎With regard to the dual properties‎, ‎the problem is presented as a measure theoretical optimization problem‎, ‎in which the existence of the solution is guaranteed‎. ‎Then‎, ‎on the basis of the atomic measure properties‎, ‎a computation method was presented for obtaining the near optimal so...

Simulated Annealing and Genetic Algorithm are two well-known metaheuristic algorithms for combinatorial optimization. These two methods have also been used for solving constrained continuous problems. In this study, five constrained continuous problems have been solved both Simulated Annealing (SA) and Genetic Algorithm (GA). Optimum results have been compared with real optimum values obtained ...

The p-median problem is one of the discrete optimization problem in location theory which aims to satisfy total demand with minimum cost. A high-level algorithmic approach can be specialized to solve optimization problem. In recent years, meta-heuristic methods have been applied to support the solution of Combinatorial Optimization Problems (COP). Collision Bodies Optimization algorithm (CBO) a...