In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...

When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multi-objective optimization. It maintains two co-evolving archives simultaneously: one, denoted...

This paper presents two memetic algorithms to solve multi-objective min-max problems, such as the ones that arise in evidence-based robust optimization. Indeed, the solutions that minimize the design budgets are robust under epistemic uncertainty if they maximize the belief in the realization of the value of the design budgets. Thus robust solutions are found by minimizing with respect to the d...

The set points of supervisory control strategy are optimized with respect to energy use and thermal comfort for existing HVAC systems. The set point values of zone temperatures, supply duct static pressure, and supply air temperature are the problem variables, while energy use and thermal comfort are the objective functions. The HVAC system model includes all the individual component models dev...

The two-archive 2 algorithm (Two_Arch2) is a many-objective evolutionary for balancing the convergence, diversity, and complexity using diversity archive (DA) convergence (CA). However, individuals in DA are selected based on traditional Pareto dominance which decreases selection pressure high-dimensional problems. even cannot converge due to weak pressure. Meanwhile, Two_Arch2 adopts as output...

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...

distribution centers (dcs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.this paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. an evolutionary algorithm named non-dominated sorting ant colony optimization (nsaco) is used as the optimi...