The twin-screw configuration problem (TSCP) arises in the context of polymer processing, where twinscrew extruders are used to prepare polymer blends, compounds or composites. The goal of the TSCP is to define the configuration of a screw from a given set of screw elements. The TSCP can be seen as a sequencing problem as the order of the screw elements on the screw axis has to be defined. It is...

a multi objective honey bee mating optimization (hbmo) designed by online learning mechanism is proposed in this paper to optimize the double fuzzy-lead-lag (fll) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. the proposed double fll stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the ...

Multi-objective evolutionary algorithms (MOEAs) have been the subject of a large research effort over the past two decades. Traditionally, these MOEAs have been seen as monolithic units, and their study was focused on comparing them as blackboxes. More recently, a component-wise view of MOEAs has emerged, with flexible frameworks combining algorithmic components from different MOEAs. The number...

Multiple objective genetic algorithms (MOGAs) simultaneously optimize a control law and geometrical features of a set of homopolar magnetic bearings (HOMB) supporting a generic flexible, spinning shaft. The minimization objectives include shaft dynamic response (vibration), actuator mass and total actuator power losses. Levitation of the spinning rotor and dynamic stability are constraint condi...

In this paper, we propose new depth-first heuristic search algorithms to approximate the set of Pareto optimal solutions in multi-objective constraint optimization. Our approach builds upon recent advances in multi-objective heuristic search over weighted AND/OR search spaces and uses an -dominance relation between cost vectors to significantly reduce the set of non-dominated solutions. Our emp...

The Multi-objective transportation problem (MOTSP) is a of linear type optimization problem which contains all equality type constraints and the multi variable objectives. Here we have presented an application of Evolutionary Algorithms to the multi-objective transportation problem (MOTSP). We consider the multi-objective transportation problem as linear optimization problem and use a special t...

Many optimization problems consist of several mutually dependent subproblems, where the resulting solutions must satisfy all requirements. We propose a new model for Multi-Objective Optimization (MOO) in Evolutionary Algorithms (EAs). The search space is partitioned into so-called Satissability Classes (SC), where each region represents the quality of the optimization criteria. Applying the SCs...

In this paper, we propose to integrate particle swarm optimization algorithm into cultural algorithms frame to develop a more efficient cultural particle swarm algorithms (CPSA) for constrained multi-objective optimization problem. In our CPSA, the population space of cultural algorithms consists of n+1 subswarms which are used to search for the n single-objective optimums and an additional mul...

This paper applies genetic algorithms (GAs), a powerful general-purpose biologically motivated optimization technique, to the multi-objective problem of spectrum optimization. Two objectives, color and efficiency, are address using real spectra, although the addition of other objectives (e.g., color rendering, color temperature) is relatively straightforward. The direct application of the metho...