A Theory of Displaced Ideals: An Analysis of Interdependent Decisions via Nonlinear Multiobjective Optimization

1979 ◽  
Vol 11 (10) ◽  
pp. 1165-1178 ◽  
Author(s):  
P Nijkamp
Keyword(s):  
Environmental Policy ◽  
Multiobjective Optimization ◽  
Geometric Programming ◽  
Multiple Objectives ◽  
Environmental System ◽  
Central Focus ◽  
Ideal Approach

This paper is devoted to a discussion of the use of nonlinear multiobjective models for the analysis of environmental policy. The central focus of the paper is on an interactive procedure by way of a so-called displaced ideal approach. The conflicting nature of multiple objectives in a spatial and environmental system is analyzed by means of a spatial variant of the ‘keeping up with the Joneses' effect. Geometric programming appears to be a useful tool to solve these nonlinear spatial—environmental multiobjective models.

Efficient Evolution of Modular Robot Control via Genetic Programming

2013 ◽  
pp. 59-85
Author(s):  
Tüze Kuyucu ◽  
Ivan Tanev ◽  
Katsunori Shimohara
Keyword(s):  
Multiobjective Optimization ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Fast Moving ◽  
Search Space ◽  
Multiple Objectives ◽  
Experimental Results ◽  
Robotic Systems ◽  
Linear Fashion ◽  
Fast Evolution

In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and a multiobjective optimization. The authors demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. They investigate the effect of the aforementioned mechanisms on the efficiency of GP employed for the coevolution of locomotion gaits and sensing of a simulated snake-like robot (Snakebot). Experimental results show that the mechanisms set forth contribute to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots as well as the robustness of the final designs.

A Joint Probability Approach to Multiobjective Optimization Under Uncertainty

2010 ◽  
Author(s):  
Sirisha Rangavajhala ◽  
Sankaran Mahadevan
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
System Performance ◽  
Optimization Problems ◽  
Joint Probability ◽  
Multiple Objectives ◽  
Optimization Under Uncertainty ◽  
System Level ◽  
Maximum Probability ◽  
Mean Values

In this paper, we present a new approach to solve optimization problems with multiple objectives under uncertainty. Optimality is considered in terms of the risk that the overall system performance, as defined by all of the multiple objectives exceeding their desired thresholds, remains acceptable. Unlike the existing state-of-the-art, where first-order moments of the system level objectives are used to ensure optimality, we employ a joint probability formulation in our research. The Pareto optimality criterion under uncertainty is defined in terms of joint probability, i.e., probability that all system objectives are less than the desired thresholds. These thresholds can be viewed as the desired upper/lower bounds on the individual system objectives. The higher the joint probability, the more reliably the thresholds bound the system performance, hence the lower the overall system performance risk. However, a desirable high joint probability may necessitate undesirably high/low thresholds, and hence the tradeoff. In this context, the proposed method provides two decision-making capabilities: (1) Maximum probability design: given a set of threshold values for system objectives, find the design that yields the maximum joint probability (2) Optimum threshold design: Given a desired joint probability, find the set of thresholds that yield this probability. In this paper, optimization formulations are presented to solve the above two decision-making problems. A two-bar truss example and the conceptual design of a two-stage-to-orbit launch vehicle are presented to illustrate the proposed methods. The numerical results show that optimizing the mean values of the objectives individually does not necessarily guarantee the desired performance of all objectives jointly under uncertainty, which is of ultimate interest in multiobjective optimization.

scholarly journals Fifth level of organisation of environmental protection systems: International law. Evolution of institutions of environmental global policy

2019 ◽  
Vol 6 (6) ◽  
pp. 41-47
Author(s):  
Dmitry M. Astanin
Keyword(s):  
Environmental Policy ◽  
International Law ◽  
Environmental Protection ◽  
Biological Diversity ◽  
Environmental Law ◽  
Environmental System ◽  
The World ◽  
Global Environmental Policy ◽  
Modern Classification ◽  
Multi Level

The analysis of the historical process of the formation of the global environmental policy of the modern states of the world in the context of the development of a multi-level environmental system is carried out. The main influence of the first International Environmental Conference in Bern 1914 on the organisation of interstate environmental authorities, the creation of the United Nations for approval of the Stockholm Declaration of 1972 and the Rio de Janeiro Declaration of 1992, which formed the modern classification of objects of environmental law, forms international eco-cooperation, ranking system of environmental policy. The thesis of the need for mutual coordination of all the participants in a multi-level environmental process, the inability of modern environmental authorities to effectively solve tasks in view of the lack of a joint action program of the world environmental system was put forward. Keywords: Environmental policy, environmental protection system, environmental law, international law, landscape and biological diversity.

A Modified Game Theory Approach to Multiobjective Optimization

1988 ◽  
Author(s):  
T. I. Freiheit ◽  
S. S. Rao
Keyword(s):  
Game Theory ◽  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Theory Approach ◽  
Vector Form ◽  
Multiobjective Problem ◽  
Design Variables ◽  
Multiobjective Optimization Problems ◽  
Competitive Economy

Abstract In todays competitive economy, it is no longer possible for the designer to find solutions to conflicting objective problems which are merely adequate. The designer must select the design variables such that there is a superior compromise between the objectives. To this end, a large number of methods have been developed for the solution of multiobjective optimization problems. The earliest reported in-depth work on the formulation of the multiobjective problem is that of Kuhn and Tucker [1]. Reviews of the progress in the field have been published by Hwang and Masud [2], Stadler [3], Evans [4], and Rastrigin and Eiduk [5]. The methods for optimizing multiple objectives may be categorized into two types, leaving the objectives in vector form and scalarizing the objectives into one equation.

Multiobjective Optimization

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 47 ◽  
Author(s):  
Matthias Ehrgott
Keyword(s):  
Decision Making ◽  
Combinatorial Optimization ◽  
Multiobjective Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Combinatorial Optimization Problems ◽  
Multiobjective Optimization Problems ◽  
Single Objective

Using some real world examples I illustrate the important role of multiobjective optimization in decision making and its interface with preference handling. I explain what optimization in the presence of multiple objectives means and discuss some of the most common methods of solving multiobjective optimization problems using transformations to single objective optimisation problems. Finally, I address linear and combinatorial optimization problems with multiple objectives and summarize techniques for solving them. Throughout the article, I refer to the real world examples introduced at the beginning.

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