We present a 250-line Matlab code for topology optimization linearized buckling criteria. The is conceived to handle stiffness, volume and load factors (BLFs) either as the objective function or constraints. use Kreisselmeier-Steinhauser aggregation in order reduce multiple objectives (viz. constraints) single, differentiable one. Then, problem sequentially approximated by using MMA-like expans...

As of today, taking into account the realities war in country and prospects for development special-purpose information communication network management system, it is possible to assert presence a stable trend growth volume improvement quality telecommunication services provided. This leads an increase quantity control information, which, turn, disproportionate special purpose system itself. Th...

In this paper a Population-based Ant Colony Optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all non-dominated solutions found so far. We investigate different maximum sizes for this population. The algorithm employs one pheromone matrix for each type of optimization criterion. The matrices are derived fr...

This work describes multibody modelling and multi-objective optimization of a six-wheeled vehicle and its motion controllers. Compliant contact models are used in order to suitably represent the rolling/slipping regime at the wheelterrain interaction. The multibody dynamic simulation model simulates multiple cases of the vehicle travelling on a soft terrain. The parameters of this model can be ...

We explore the feasibility of applying SAT solvers to optimizing implementations of small functions such as S-boxes for multiple optimization criteria, e.g., the number of nonlinear gates and the number of gates. We provide optimized implementations for the S-boxes used in Ascon, ICEPOLE, Joltik/Piccolo, Keccak/Ketje/Keyak, LAC, Minalpher, PRIMATEs, Prøst, and RECTANGLE, most of which are candi...

Currently a standard technique to compute the heuristic in heuristic planning is to expand a planning graph on the relaxed problem. This paper presents a new approach to expand the planning graph, such that heuristic estimations are more accurate when an optimization metric criteria is given. Additionally, a new kind of Hill-Climbing search that combines two heuristics is proposed to deal with ...