A novel multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) framework is proposed to utilize a minimal number of training samples efficiently for sequential model updates. All the sample points are enforced to be feasible, and to provide coverage of sparsely explored sparse design regions using a new optimization subproblem. The MO-ASMO method only evaluates high-fidelity fu...

For optimization of real-world arch dams, it is unavoidable to consider two or more conflicting objectives. This paper employs two multi-objective differential evolution algorithms (MoDE) in combination of a parallel working MATLAB-APDL code to obtain a set of Pareto solutions for optimal shape of arch dams. Full dam-reservoir interaction subjected to seismic loading is considered. A benchmark ...

Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front) reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by sta...

In multi-objective optimization, the knowledge of the Pareto set provides valuable information on the reachable optimal performance. A number of evolutionary strategies (PAES [4], NSGA-II [1], etc), have been proposed in the literature and proved to be successful to identify the Pareto set. However, these derivative-free algorithms are very demanding in terms of computational time. Today, in ma...

With growing interest in recovering materials and subassemblies within consumer products at the end of their useful life, there has been an increasing interest in developing decision-making methodologies that determine how to maximize the environmental benefits of end-of-life (EOL) processing while minimizing costs under variable EOL situations. This paper describes a methodology to analyze how...

Given a range of traffic related sustainability problems, policy makers need to know which measures should be taken to reach their objectives as much as possible. Multi-objective optimisation is useful to support these decisions, because it results in an overview of possibly optimal solutions. This Pareto set can be very large, especially if more than two (mainly opposed) objectives are involve...

in this paper, lift and drag coefficients were numerically investigated using numeca software in a set of 4-digit naca airfoils. two metamodels based on the evolved group method of data handling (gmdh) type neural networks were then obtained for modeling both lift coefficient (cl) and drag coefficient (cd) with respect to the geometrical design parameters. after using such obtained polynomial n...

In this paper, we consider the main algorithmic difficulties of constructing a Pareto set associated with configuration an array points. On basis coordinate approach «maximin» author’s algorithm construction is made, taking into account these difficulties. The program written in Python. was graphically tested on specific example: sample minimum total price and high «quality» (Harrinkton desirab...

This paper presents the extension of the Neural Evolutional Strategy System (Nessy) to the multiobjective optimization case. The neural architecture of the Nessy algorithm is extended by using more than one output neuron, one neuron for each objective. The learning law of Nessy is modified according to the presence of multiple measures of performance. Each hidden neuron of the generation layer ...