In this work, it is described a gait multiobjective optimization system that allows to obtain fast but stable robot quadruped crawl gaits. We combine bioinspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). A motion architecture based on CPGs oscillators is used to model the locomotion of the robot dog and a GA is used to search parameterizations of the CPGs parameters which ...

A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP). The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization problem by using scalar approach, and then the whole efficient set of the BLBOP is derived by the proposed two-stage ANN for exploring the induced...

a problem that sometimes occurs in multiobjective optimization is the existence of a large set of fairly effcient solutions. hence, the decision making based on selecting a unique preferred solution is diffcult. considering models with fair b-effciency relieves some of the burden from the decision maker by shrinking the solution set, since the set of fairly b-efficient solutions is contained with...

Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. The first part of this work proposes a formulation o...

Abstract In this paper we present results on dynamic multivariate scalar risk measures, which arise in markets with transaction costs and systemic risk. Dual representations of such measures are presented. These then used to obtain the main time consistency; namely, an equivalent recursive formulation multiportfolio consistency. We motivated study consistency as superhedging measure (with a sin...

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) transforms a optimization problem (MOP) into set of single-objective subproblems for collaborative optimization. Mismatches between and solutions can lead to severe performance degradation MOEA/D. Most existing mismatch coping strategies only work when the L∞ scalarization is used. A strategy that use any Lp scalarization, ...