This work proposes Adaptive e-Ranking to enhance Pareto based selection, aiming to develop effective many-objective evolutionary optimization algorithms. eRanking fine grains ranking of solutions after they have been ranked by Pareto dominance, using a randomized sampling procedure combined with e-dominance to favor a good distribution of the samples. In the proposed method, sampled solutions k...

There are currently three different game strategies to solve multi-criteria design problems: 1) Cooperative Games (Pareto Front), 2) Competitive Games (Nash Game) and 3) Hierarchical Games (Stackelberg Game). Nowadays EAs and Pareto front concept are more and more in solving practical design problems in industry, but this process is time consuming. Here, deterministic optimization method and Pa...

1 AbstractIn this paper, a new distributed genetic algorithm for multiobjective optimization problems is proposed. In this approach, the island model is used with a distributed genetic algorithm and an operation of sharing for Pareto-optimum solutions is performed with the total population. In multiobjective optimization problems, the Pareto-optimum solutions should be derived for designers. Be...

In spite of the recent quick growth of the Evolutionary Multi-objective Optimization (EMO) research field, there has been few trials to adapt the general variation operators to the particular context of the quest for the Pareto-optimal set. The only exceptions are some mating restrictions that take in account the distance between the potential mates – but contradictory conclusions have been rep...

This paper presents an application of an evolutionary optimization algorithm for multiobjective analysis for reservoir operations and planning. A multiobjective particle swarm optimization (MOPSO) algorithm is used to find nondominated solutions with four objectives: (i) maximize annual firm water supply; (ii) maximize annual firm energy production; (iii) minimize flood risk; and (iv) maximize ...

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multi-objective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the true Paretooptimal solutions with a widely spread distribution of solut...

The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that these techniques sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new line search based approach for multicriteria optimization. The objectives are aggregated and the problem is transformed into a single objective...

A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist-mutation (EM) operator are introduced. The proposed EM-...

A visualization methodology is presented in which a Pareto Frontier can be visualized in an intuitive and straightforward manner for an n-dimensional performance space. Based on this visualization, it is possible to quickly identify ‘good’ regions of the performance and optimal design spaces for a multi-objective optimization application, regardless of space complexity. Visualizing Pareto solut...

Configuration design of mechanical systems corresponds to finding the placement of a set of components such that performance criteria are optimized while satisfying design constraints. In this paper, the configuration design of a midsize truck for optimum vehicle dynamic behavior, survivability, and maintainability is considered within a multi-criteria decision making framework in which decisio...