Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, widely used approach is based on the Pareto front. In this document, proposal made analysis of optimal front problems using clustering techniques. With approach, an sought further use and improvement considering solutions clusters found. To carry out clustering, metho...

In order to deal with constrained multi-objective optimization problems (CMOPs), a novel constrained multi-objective particle swarm optimization (CMOPSO) algorithm is proposed based on an adaptive penalty technique and a normalized non-dominated sorting technique. The former technique is utilized to optimize constrained individuals in each generation to obtain new objective functions, while the...

Reactor core design is inherently a multi-objective problem which spans large space, and potentially larger objective space. This process relies on high-fidelity models to probe the sophisticated computer codes calculate important physics occurring in reactor. In past, space has been reduced by individuals with extensive knowledge of reactor design; however, this approach not always available. ...

This paper uses integrated Data Envelopment Analysis (DEA) models to rank all extreme and non-extreme efficient Decision Making Units (DMUs) and then applies integrated DEA ranking method as a criterion to modify Genetic Algorithm (GA) for finding Pareto optimal solutions of a Multi Objective Programming (MOP) problem. The researchers have used ranking method as a shortcut way to modify GA to d...

This paper introduces the Pareto front as a useful analysis tool to explore the design space of MOS Current Mode Logic (MCML) circuits. A genetic algorithm (GA) is employed to automatically detect this front in a process that efficiently finds optimal parameterizations and their corresponding values in an aggregate fitness space. As an example of the flexibility of this design automation approa...

This paper proposes an improved evolutionary algorithm with parallel evaluation strategy (EAPES) for solving constrained multi-objective optimization problems (CMOPs) efficiently. EAPES stores feasible solutions and infeasible solution separately in different populations, and evaluates infeasible solutions in an unusual manner, such that not only feasible solutions but also useful infeasible so...

In this paper, two algorithms have been developed for allocation and size determination of Active Power Filters (APF) in power systems. In the first algorithm, the objective is to minimize harmonic voltage distortion. The objective in the second algorithm is to minimize the new APF injection currents while satisfying harmonic standards. Genetic algorithm is proposed for these two optimization p...

In this paper, two algorithms have been developed for allocation and size determination of Active Power Filters (APF) in power systems. In the first algorithm, the objective is to minimize harmonic voltage distortion. The objective in the second algorithm is to minimize the new APF injection currents while satisfying harmonic standards. Genetic algorithm is proposed for these two optimization p...

This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the mult...

A new tool is developed in order to solve computationally expensive multi-objective topology optimization problems related to the design for flexible active and passive skins for morphing aircraft. The approach used is based on a multiobjective genetic algorithm coupled with a local search algorithm to create a hybrid multi-objective algorithm. The ability of the developed algorithm to find eff...