In this paper, a fractional order (FO) PID controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing ...

This paper addresses an unrelated multi-machine scheduling problem with sequence-dependent setup time, release date and processing set restriction to minimize the sum of weighted earliness/tardiness penalties and the sum of completion times, which is known to be NP-hard. A Mixed Integer Programming (MIP) model is proposed to formulate the considered multi-criteria problem. Also, to solve the mo...

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researche...

An algorithm to achieve maximal spread and almost perfectly distributed Pareto fronts is presented. The MaxiMin algorithm add points to the archive of selected individuals one by one, each point which is added maximizes the distance from the current selected points. This method is independent of the evolutionary operators used to perform the search. This work explains how to combine the MaxiMin...

Evolutionary multi-objective optimization (EMO) found applications in all fields of science and engineering. Chemical engineering discipline is no exception. Literature abounds on EMO with a variety algorithms proposed by few dedicated researchers. The Nondominated Sorting Genetic Algorithm (NSGA-III) the latest addition to family EMO. NSGA-III claims have solved multi many-objective problems u...

Abstract The ground station scheduling problem is a complex involving multiple objectives. Evolutionary techniques for multi-objective optimization are becoming popular among different fields, due to their effectiveness in obtaining set of trade-off solutions. In contrast some conventional methods, that aggregate the objectives into one weighted-sum objective function, evolutionary algorithms m...

There has emerged a surge of research activity on multiobjective optimization using evolutionary computation in recent years and a number of well performing algorithms have been published. The quick and highly efficient multiobjective evolutionary algorithm based on dominating tree has been criticized mainly for its restricted elite archive and absence of density estimation. This paper improves...

This paper presents a new multi-objective evolutionary algorithm based on differential evolution. The proposed approach adopts a secondary population in order to retain the non-dominated solutions found during the evolutionary process. Additionally, the approach also incorporates the concept of ε-dominance to get a good distribution of the solutions retained. The main goal of this work was to k...

Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering ...

In Multi-objective Optimization the goal is to present a set of Pareto-optimal solutions to the decision maker (DM). One out of these solutions is then chosen according to the DM preferences. Given that the DM has some general idea of what type of solution is preferred, a more efficient optimization could be run. This can be accomplished by letting the optimization algorithm make use of this pr...