In the multi objective problems, the good Pareto optimum solutions should have the following characteristics; the solutions should be close to the real Pareto front, the solutions should not be concentrated but should be widespread and the solutions should have the optimum solutions of every single objective function. ”Distributed Cooperation model of Multi-Objective Genetic Algorithm (DCMOGA)”...

A genetic algorithm for multiobjective optimization is presented which tries to evolve an evenly distributed set of solutions belonging to the Pareto set by: (i) ranking the population according to nondomination properties; (ii) defining a filter to retain Pareto set solutions and (iii) using adequate operators: exclusion, addition and single-objective operator which improves the individuals fr...

This paper reports an evolutionary approach called Scatter Search, that uses the concept of Pareto optimality to obtain a good approximate Pareto frontier. In order to designate a subset of strategies to be a reference solutions a choice function called Kramer Selection is used. A variant of measure of Kemen-Snell may be used, in our case study, in order to find a diverse set to complement the ...

For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set. Unfortunately, these sets are often large and can become difficult to comprehend and consider. Two methods are presented as practical approaches to reduce the size of the Pareto optimal set for multiple-objective system reliability design problems. The first method is a pseudo-ranki...

This article proposes a novel Pareto-based multiobjective meta-heuristic algorithm named non-dominated ranking genetic algorithm (NRGA) to solve multi-facility location-allocation problem. In NRGA, a fitness value representing rank is assigned to each individual of the population. Moreover, two features ranked based roulette wheel selection including select the fronts and choose solutions from ...

Most of the engineering problems are modeled as evolutionary multiobjective optimization problems, but they always ask for only one best solution, not a set of Pareto optimal solutions. The decision maker’s subjective information plays an important role in choosing the best solution from several Pareto optimal solutions. Generally, the decision-making processing is implemented after Pareto opti...

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...

Many real-world optimization problems have several, usually conflicting objectives. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions, and relies on a decision maker to finally select a single solution. However, in particular if the number of objectives is large, the number of Pareto-optimal solutions may be hu...

Pareto local search (PLS) methods are local search algorithms for multiobjective combinatorial optimization problems based on the Pareto dominance criterion. PLS explores the Pareto neighbourhood of a set of non-dominated solutions until it reaches a local optimal Pareto front. In this paper, we discuss and analyse three different Pareto neighbourhood exploration strategies: best, first, and ne...

in this paper an approach based on evolutionary algorithms to find pareto optimal pair of state and control for multi-objective optimal control problems (moocp)'s is introduced‎. ‎in this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...