For multi-objective optimal reactive power dispatch (MORPD), a new approach is proposed where simultaneous minimization of the active power transmission loss, the bus voltage deviation and the voltage stability index of a power system are achieved. Optimal settings of continuous and discrete control variables (e.g. generator voltages, tap positions of tap changing transformers and the number of...

Cross docking is a logistics strategy that strives to reduce inventory holding costs, shipping costs, and delays in delivering the products. In this research, an optimization model is presented for split loading and unloading products by suppliers and customers, vehicle routing with fuzzy possibilistic time window constraints among them, assignment of vehicles to cross dock, consolidation and i...

the present paper aims to investigate the effects of modularity and the layout of subsystems and parts of a complex system on its maintainability. for this purpose, four objective functions have been considered simultaneously: i) maximizing the level of accordance between system design and optimum modularity design,ii) maximizing the level of accessibility and the maintenance space required,iii...

In many real-world optimization problems sparse solution vectors are often preferred. Unfortunately, evolutionary algorithms can have problems to eliminate certain components completely especially in multi-modal or neutral search spaces. A simple extension of the realvalued representation enables evolutionary algorithms to solve these types of optimization problems more efficiently. In case of ...

in this paper an approach based on evolutionary algorithms to find pareto optimal pair of state and control for multi-objective optimal control problems (moocp)'s is introduced‎. ‎in this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...

Mixed-model assembly lines are increasingly accepted in many industrial environments to meet the growing trend of greater product variability, diversification of customer demands, and shorter life cycles. In this research, a new mathematical model is presented considering balancing a mixed-model U-line and human-related issues, simultaneously. The objective function consists of two separate com...

Evolutionary Computation (EC) is the field of computational systems that use ideas and get inspiration from natural evolution [1]. Genetic Algorithms (GA) fall into the category of EC. GA are a type of search and optimization algorithm based on the mechanisms of genetics and natural selection. The canonical form of GA encodes each candidate solution to a given problem as a binary, integer, or r...

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling...

Estimation of distribution algorithms have been shown to perform well on a wide variety of single–objective optimization problems. Here, we look at a simple yet effective extension of this paradigm for multi–objective optimization, called the naive MIDEA. The probabilistic model in this specific algorithm is a mixture distribution, and each component in the mixture is a univariate factorization...

For optimization of real-world arch dams, it is unavoidable to consider two or more conflicting objectives. This paper employs two multi-objective differential evolution algorithms (MoDE) in combination of a parallel working MATLAB-APDL code to obtain a set of Pareto solutions for optimal shape of arch dams. Full dam-reservoir interaction subjected to seismic loading is considered. A benchmark ...