Search results for "monitavoiteoptimointi"
showing 10 items of 81 documents
Value of information in multiple criteria decision making: an application to forest conservation
2019
Abstract Developing environmental conservation plans involves assessing trade-offs between the benefits and costs of conservation. The benefits of conservation can be established with ecological inventories or estimated based on previously collected information. Conducting ecological inventories can be costly, and the additional information may not justify these costs. To clarify the value of these inventories, we investigate the multiple criteria value of information associated with the acquisition of improved ecological data. This information can be useful when informing the decision maker to acquire better information. We extend the concept of the value of information to a multiple crite…
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
2017
Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approxim…
DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
2021
Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive …
Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker
2021
AbstractSolving multiobjective optimization problems with interactive methods enables a decision maker with domain expertise to direct the search for the most preferred trade-offs with preference information and learn about the problem. There are different interactive methods, and it is important to compare them and find the best-suited one for solving the problem in question. Comparisons with real decision makers are expensive, and artificial decision makers (ADMs) have been proposed to simulate humans in basic testing before involving real decision makers. Existing ADMs only consider one type of preference information. In this paper, we propose ADM-II, which is tailored to assess several …
DESDEO : An Open Framework for Interactive Multiobjective Optimization
2018
We introduce a framework for interactive multiobjective optimization methods called DESDEO released under an open source license. With the framework, we want to make interactive methods easily accessible to be applied in solving real-world problems. The framework follows an object-oriented software design paradigm, where functionalities have been divided to modular, self-contained components. The framework contains implementations of some interactive methods, but also components which can be utilized to implement more interactive methods and, thus, increase the applicability of the framework. To demonstrate how the framework can be used, we consider an example problem where the pollution of…
An Interactive Framework for Offline Data-Driven Multiobjective Optimization
2020
We propose a framework for solving offline data-driven multiobjective optimization problems in an interactive manner. No new data becomes available when solving offline problems. We fit surrogate models to the data to enable optimization, which introduces uncertainty. The framework incorporates preference information from a decision maker in two aspects to direct the solution process. Firstly, the decision maker can guide the optimization by providing preferences for objectives. Secondly, the framework features a novel technique for the decision maker to also express preferences related to maximum acceptable uncertainty in the solutions as preferred ranges of uncertainty. In this way, the d…
A New Paradigm in Interactive Evolutionary Multiobjective Optimization
2020
Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving multiobjective optimization problems in an interactive manner by using multiple scalarization functions to map vectors in the objective space to a new, so-called preference incorporated space (PIS). In this way, the original problem is converted into a new multiobjective optimization problem with typically fewer objectives in the PIS. This mapping enables a modular incorporation of decision maker’s preferences to convert any evolutionary algorithm to an interactive one, whe…
Interview: Kalyanmoy Deb Talks about Formation, Development and Challenges of the EMO Community, Important Positions in His Career, and Issues Faced …
2023
Kalyanmoy Deb was born in Udaipur, Tripura, the smallest state of India at the time, in 1963 [...]
Register data in sample allocations for small-area estimation
2018
The inadequate control of sample sizes in surveys using stratified sampling and area estimation may occur when the overall sample size is small or auxiliary information is insufficiently used. Very small sample sizes are possible for some areas. The proposed allocation based on multi-objective optimization uses a small-area model and estimation method and semi-collected empirical data annually collected empirical data. The assessment of its performance at the area and at the population levels is based on design-based sample simulations. Five previously developed allocations serve as references. The model-based estimator is more accurate than the design-based Horvitz–Thompson estimator and t…
Artificial Decision Maker Driven by PSO : An Approach for Testing Reference Point Based Interactive Methods
2018
Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization f…