quadruped robots have unique capabilities for motion over uneven natural environments. this article presents a stable gait for a quadruped robot in such motions and discusses the inverse-dynamics control scheme to follow the planned gait. first, an explicit dynamics model will be developed using a novel constraint elimination method for an 18-dof quadruped robot. thereafter, an inverse-dynamics...

The automotive deployment problem is a real-world constrained multiobjective assignment problem in which software components must be allocated to processing units distributed around a car’s chassis. Prior work has shown that evolutionary algorithms such as NSGA-II can produce good quality solutions to this problem. This paper presents a population-based ant colony optimisation (PACO) approach t...

Optimization of the whole plant instead of important individual units is essential for maximizing savings and operational efficiency. Often, there are conflicting objectives for optimizing industrial processes. Many previous studies on multi-objective optimization involved a few critical units (and not complete plants) using models and simulation programs specifically developed for the respecti...

In this paper, the topic of constrained multiobjective in-core fuel management optimisation (MICFMO) using metaheuristics is considered. Several modern and stateof-the-art metaheuristics from different classes, including evolutionary algorithms, local search algorithms, swarm intelligence algorithms, a probabilistic model-based algorithm and a harmony search algorithm, are compared in order to ...

Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but existing researches concentrate more on its application in single-objective optimization. In this paper, we focus on improving the algorithm to solve the multi-objective problem and increasing its convergence speed. The proposed algorithm is named as multi-objective Particle Swarm Marriage in Honey Bees Optimizati...

Multiobjective optimization is increasingly used in engineering to design new systems and identify tradeoffs. Yet, problems often have objective functions constraints that are expensive highly nonlinear. Combinations of these features lead poor convergence diversity loss with common algorithms not been specifically designed for constrained optimization. Constrained benchmark exist, but they do ...

Engineering design optimization problems increasingly require computationally expensive high-fidelity simulation models to evaluate candidate designs. The evaluation budget may be small, limiting the effectiveness of conventional multi-objective evolutionary algorithms. Bayesian algorithms (BOAs) are an alternative approach for but underdeveloped in terms support constraints and non-continuous ...

We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multiobjective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, a...