Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary algorithms to approximate the optimal solutions of large-scale multiobjective optimization problems (LMOPs) by using a limited budget of evaluations. If the Pareto-optimal subspace is approximated during the...
It even discovered new best-ever solutions to four problems. The first-generation AIM computer, built last year, solves QUMO optimization problems that are represented with an accuracy of up to 7 bits. The team, shown in Figure 3, has also shown good quant...
Mebarki, N., A. Dussauchoy and H. Pierreval, 1998. On the comparison of solutions in stochastic simulation-optimization problems with several performance measures. Int. Trans. Operat. Res., 5: 137-145. DOI: 10.1111/j.1475- 3995.1998.tb00109.x...
highly innovative algorithm are still deemed necessary, as per the No Free Lunch (NFL) theorem7. The NFL theorem asserts that the superior performance of a metaheuristic algorithm in solving specific optimization problems does not guarantee similar success in solving different problems. Therefore...
Optimization is a process that finds the “best” possible solutions from a set of feasible solutions(在可行解中寻找最优解的过程) Meaning of "best" can vary("最优"的定义是多样的) Definition: what is an optimization problem A mathematical problem of finding the best possible solution from a ...
Within this category, we may consider two divisions of optimization problems. The first branch is called “integer programming,” where the discrete set of the feasible solutions is a subset of integers. This class of models is mostly common as many real-life applications are modeled with ...
Combinatorial optimization (CO) problems are an important class of problems where the number of possible solutions grows combinatorially with the problem size. These kinds of problems have attracted the attention of researchers in computer sciences, operational research, and artificial intelligence. The fu...
In both presented examples, using a machine equipped with an AMD RYZEN THREADRIPPER 1950X processor, simulation of every unit cell (i.e., every candidate solution of the problems) took about 100 seconds using 500 MB of memory. Notice that in the proposed method, parallel simulation of ...
An optimization problem can be generally defined as finding the best solution to a mathematical problem from all feasible solutions. The methods used in optimization vary depending on the type of problem and the variables involved. Optimization problems with discrete variables are known as combinatorial...
In challenging optimization problems (nonlinear, nonconvex, etc.). Algorithms that use one solution or a set of solutions create the final class of metaheuristics. It is considered a single-solution algorithm if an algorithm starts, improves, and finishes with a single solution. If more than ...