In this paper, we propose a novel gearbox design method, called multiple optimization (MGO), that can simultaneously patterns of feasible gearbox. This MGO consists penalty handling method and multimodal method. The converts an existing constrained problem to unconstrained one. is developed by making our previously proposed gravitational particle swarm algorithm (GPSA) solve mixed-integer probl...

We study two-stage robust optimization problems with mixed discrete-continuous decisions in both stages. Despite their broad range of applications, these problems pose two fundamental challenges: (i) they constitute infinite-dimensional problems that require a finite-dimensional approximation, and (ii) the presence of discrete recourse decisions typically prohibits dualitybased solution schemes...

Ant colony optimization (ACO) is a metaheuristic approach for combinatorial optimization problems. With the introduction of hypercube framework, invariance property of ACO algorithms draws more attention. In this paper, we propose a novel two-stage updating pheromone for invariant ant colony optimization (TSIACO) algorithm. Compared with standard ACO algorithms, TSIACO algorithm uses solution o...

MP1 : min y,η cy + η s.t. Ay ≥ d η ≥ (h−Ey −Mul ) π∗ l , ∀l ≤ k y ∈ Sy, η ∈ R. Derive an optimal solution (y∗ k+1, η ∗ k+1) and update LB = c Ty∗ k+1 + η ∗ k+1. 3. Call the oracle to solve SP1 in (2) in Section 2, i.e. Q(y∗ k+1), and derive an optimal solution (uk+1, π ∗ k+1). Update UB = min{UB, cTy∗ k+1 +Q(y∗ k+1)}. 4. If UB − LB ≤ 2, return y∗ k+1 and terminate. Otherwise, update k = k + 1 a...

Ideal portfolio creation has been the focus of considerable machine learning research in the domain of finance. In this paper, the development of a two-stage platform for generating stable stock-based portfolios is explored. The first stage involves clustering of stocks based on time-weighted correlations, using a modified version of the K-Means++ algorithm. This clustering helps in the quantif...

The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic en...

The choice of hyperparameters and the selection of algorithms is a crucial part in machine learning. Bayesian optimization methods have been used very successfully to tune hyperparameters automatically, in many cases even being able to outperform the human expert. Recently, these techniques have been massively improved by using metaknowledge. The idea is to use knowledge of the performance of a...

In this paper, we describe a two-stage method for solving optimization problems with bound constraints. It combines the active-set estimate described in [1] with a modification of the nonmonotone line search framework recently proposed in [2]. In the first stage, the algorithm exploits a property of the active-set estimate that ensures a significant reduction of the objective function when sett...

In today’s business a close operation is necessary to decrease the joint total inventory cost. According to Simchi-Levi et al (2000) several international companies have demonstrated that integrating the supply chain has improved the company’s performance and market share. This paper considers an inventory vendor-buyer integrated system in a fuzzy situation by employing the type of fuzzy number...