Commercial plant breeders improve economically important traits by selectively mating individuals from a given breeding population. Potential pairings are evaluated before the growing season using Monte Carlo simulation, and a mating design is created to allocate a fixed breeding budget across the parent pairs to achieve desired population outcomes. We introduce a novel objective function for t...

Several problems in the engineering domain are multi-objective in nature. The solution to multi-objective optimization is a set of solutions rather than a single point solution. Such a set of non-dominated solutions are called Pareto optimal solutions or non-inferior solutions. In this paper, a new algorithm, Elitist-Multi-objective Differential Evolution (E-MODE) is proposed. The proposed algo...

Design of an optimal controller requires the optimization of differential evolution performance measures that are often no commensurable and competing with each other. Being a population based approach; Differential Evolution (DE) is well suited to solve designing problem of TCSC – based controller. This paper investigates the application of DE-based multi-objective optimization technique for t...

We consider Multi Criteria Decision Making (MCDM) as the conjunction of three components: search, preference tradeoffs, and interactive visualization. The first MCDM component is the search process over the space of possible solutions to identify the nondominated solutions that compose the Pareto set. The second component is the preference tradeoff process to select a single solution (or a smal...

Evolutionary optimization algorithms work with a population of solutions, instead of a single solution. Since multi-objective optimization problems give rise to a set of Pareto-optimal solutions, evolutionary optimization algorithms are ideal for handling multi-objective optimization problems. Over many years of research and application studies have produced a number of efficient multi-objectiv...

Requirements prioritisation is a key decision making activity of the software development process, which relies on the capability of different decision-makers to identify the optimal candidate rankings of the requirements, in order to be able to perform a strategic choice among them. In this paper, we formulate such multi-decision-maker requirements prioritisation as a multi-objective optimisat...

A Pareto-optimal solution is developed in this paper for a scheduling problem on a single machine with periodic maintenance and non-preemptive jobs. Most of the scheduling problems address only one objective function, while in the real world, such problems are always associated with more than one objective. In this paper, both multi-objective functions and multi-maintenance periods are consider...

in this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. scheduling algorithms play an important role in grid computing, parallel tasks scheduling and sending them to appr...

The main method of solving multi-objective programming is changing multi-objective programming problem into single objective programming problem, and then get Pareto optimal solution. Conversely, whether all Pareto optimal solutions can be obtained through appropriate method, generally the answer is negative. In this paper, the methods of norm ideal point and membership function are used to sol...