We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FPGA). FPGA uses a new ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally expensive. New genetic operators are employed to enhance the algorithm’s performance in terms of convergence behavior and computational effort. Compu...

Multiobjective optimization of a set of N identical reverse-flow cyclone separators in parallel was carried out by using the nondominated sorting genetic algorithm (NSGA). Two objective functions were used: the maximization of the overall collection efficiency and the minimization of the pressure drop. Nondominated Pareto optimal solutions were obtained for an industrial problem in which 165 m3...

Many real world problems require careful balancing of fiscal, technical, and social objectives. Informed negotiation and balancing of objectives can be greatly aided through the use of evolutionary multiobjective optimization (EMO) algorithms, which can evolve entire tradeoff (or Pareto) surfaces within a single run. The primary difficulty in using these methods lies in the large number of para...

The increasing complexity of the modern control systems has emphasized the idea of applying new approaches in order to solve design problems for different control engineering problems. This paper reports a performance comparison between traditional (linear PID controller) and evolvable methods (evolvable hardware controllers) applied to the problem of three-degrees-of-freedom manipulator contro...

In many real-world optimization problems the quality of solutions is determined with several fundamentally different objectives, such as, for example, cost, performance and profit. These objectives are often mutually conflicting thus yielding several optimal solutions, where each of them represents a different tradeoff between the objectives. Classicalmethods solve multiobjective optimization p...

Many control problems involve simultaneous optimization of multiple performance measures that are often noncommensurable and competing with each other. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theory is applied to obtain the solution of such problems using different scalar approaches. The presence of multiple objectives in a problem usua...

A multi-objective optimization problem (MOP) is often found in real-world optimization problem. Among various multiobjective optimization techniques, multi-objective evolutionary algorithm (MOEA) is highlighted as a good candidate due to its flexibility, feasibility, and its ability to handle multiple solutions. Among various MOEAs, we analyze 2MOEA which can achieve good convergence and divers...

In Wireless Sensor Network (WSN), coverage and connectivity are the vital challenges in target-based region. The linear objective is to find positions cover complete target nodes between each sensor for data forwarding towards base station given a grid with points potential placement position. this paper, multiobjective problem on WSN (t-WSN) derived, which minimizes number of deployed n...

This paper proposes a novel Multi-Objective Evolutionary Algorithm for hardware software partitioning of embedded systems. Customized genetic algorithms (GA) have been effectively used for solving complex optimization problems (NP Hard) but are mainly applied to optimize a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objectiv...

Static Var compensator (SVC) is one of flexible AC transmission system (FACTS) elements mainly used for reactive power and voltage control in power systems. This paper deals with multi-modal electromechanical oscillations damping in the presence of severe disturbances. These oscillations include local modes, interarea modes and inter-plant modes. To enhance the damping of the oscillations, a co...