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...
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...
Sometimes you can solve a complicated problem using an evolutionary approach. First, solve problems with a smaller number of independent variables. Use solutions from these simpler problems as starting points for more complicated problems by using an appropriate mapping. Also, you can sometimes speed ...
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 ...
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...
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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 ...
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...
Additionally, the inertia weight 𝑤w intricately intertwines with the number of iterations outlined in our algorithm. The remaining consideration is to assess whether the number of particles, comprising our swarm, influences the evolution and quality of our solutions. The solutions for 28 problems ...
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