Multi-objective Satellite Selection Strategy Based on Entropy

2021 ◽  
Author(s):  
Shaofeng Zhang ◽  
Lantu Guo ◽  
Weiqing Mu ◽  
Jie Wang ◽  
Yanan Liu
Keyword(s):  
Selection Strategy ◽  
Multi Objective ◽  
Satellite Selection

scholarly journals A Spectral Clustering-Based Multi-Source Mating Selection Strategy in Evolutionary Multi-Objective Optimization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 131851-131864 ◽  
Author(s):  
Shuai Wang ◽  
Hu Zhang ◽  
Yi Zhang ◽  
Aimin Zhou ◽  
Peng Wu
Keyword(s):  
Spectral Clustering ◽  
Selection Strategy ◽  
Multi Objective Optimization ◽  
Multi Objective

The Effect of Credit Definition and Aggregation Strategies on Multi-Objective Hyper-Heuristics

2015 ◽  
Author(s):  
Nozomi Hitomi ◽  
Daniel Selva
Keyword(s):  
Optimization Problems ◽  
Entire Population ◽  
Optimization Process ◽  
Selection Strategy ◽  
Credit Assignment ◽  
Multi Objective ◽  
Domain Specific ◽  
Combinatorial Optimization Problems ◽  
Pareto Ranking ◽  
Selection Strategies

Heuristics and meta-heuristics are often used to solve complex real-world problems such as the non-linear, non-convex, and multi-objective combinatorial optimization problems that regularly appear in system design and architecture. Unfortunately, the performance of a specific heuristic is largely dependent on the specific problem at hand. Moreover, a heuristic’s performance can vary throughout the optimization process. Hyper-heuristics is one approach that can maintain relatively good performance over the course of an optimization process and across a variety of problems without parameter retuning or major modifications. Given a set of domain-specific and domain-independent heuristics, a hyper-heuristic adapts its search strategy over time by selecting the most promising heuristics to use at a given point. A hyper-heuristic must have: 1) a credit assignment strategy to rank the heuristics by their likelihood of producing improving solutions; and 2) a heuristic selection strategy based on the credits assigned to each heuristic. The literature contains many examples of hyper-heuristics with effective credit assignment and heuristic selection strategies for single-objective optimization problems. In multi-objective optimization problems, however, defining credit is less straightforward because there are often competing objectives. Therefore, there is a need to define and assign credit so that heuristics are rewarded for finding solutions with good trades between the objectives. This paper studies, for the first time, different combinations of credit definition, credit aggregation, and heuristic selection strategies. Credit definitions are based on different applications of the notion of Pareto dominance, namely: A1) dominance of the offspring with respect to the parent solutions; A2) ability to produce non-dominated solutions with respect to the entire population; A3) Pareto ranking with respect to the entire population. Two different credit aggregation strategies for assigning credit are also examined. A heuristic will receive credit for: B1) only the solutions it created in the current iteration or B2) all solutions it created that are in the current population. Different heuristic selection strategies are considered including: C1) probability matching; C2) dynamic multi-armed bandit; and C3) Hyper-GA. Thus, we conduct an experiment with three factors: credit definition (A1, A2, A3), credit aggregation (B1, B2), and heuristic selection (C1, C2, C3) and conduct a full factorial experiment. Performance is measured by hyper-volume of the last population. All algorithms are tested on a design problem for a climate monitoring satellite constellation instead of classical benchmarking problems to apply domain-specific heuristics within the hyper-heuristic.

Dynamic Economic Emission Dispatch Using Multi-Objective Hybrid Evolutionary Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2154-2158
Author(s):  
Lei Zhang ◽  
Jun Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Power Plants ◽  
Test System ◽  
Selection Strategy ◽  
Hybrid Evolutionary Algorithm ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Output Constraints ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Dynamic economic emission dispatch (DEED) is an important optimization task for power plants. The problem is a highly constrained multi-objective optimization problem involving conflicting objectives with both equality and inequality constraints. This paper introduces two objective functions of DEED model: the lowest generation cost and the smallest carbon emissions with power balance constraints, unit output constraints and unit ramp rate limits. Then the paper presents a multi-objective hybrid evolutionary algorithm (MHEA) to solve the DEED model. The MHEA is a hybrid optimization algorithm based on orthogonal initialization, improved differential operation with migration strategy, parameter adaptive control, multi-objective selection strategy and analytic hierarchy process based fuzzy technique (AFT). Numerical results of one test system demonstrate the capabilities of the proposed approach. Compared with other classical methods, the proposed approach gets better result.

scholarly journals Variation Rate to Maintain Diversity in Decision Space within Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 24 (3) ◽  
pp. 82 ◽  
Author(s):  
Oliver Cuate ◽  
Oliver Schütze
Keyword(s):  
Significant Loss ◽  
Selection Strategy ◽  
Benchmark Problems ◽  
Decision Space ◽  
Multi Objective ◽  
Approximation Quality ◽  
Selection Strategies ◽  
Objective Space ◽  
Variation Rate ◽  
Variable Space

The performance of a multi-objective evolutionary algorithm (MOEA) is in most cases measured in terms of the populations’ approximation quality in objective space. As a consequence, most MOEAs focus on such approximations while neglecting the distribution of the individuals of their populations in decision space. This, however, represents a potential shortcoming in certain applications as in many cases one can obtain the same or very similar qualities (measured in objective space) in several ways (measured in decision space). Hence, a high diversity in decision space may represent valuable information for the decision maker for the realization of a given project. In this paper, we propose the Variation Rate, a heuristic selection strategy that aims to maintain diversity both in decision and objective space. The core of this strategy is the proper combination of the averaged distance applied in variable space together with the diversity mechanism in objective space that is used within a chosen MOEA. To show the applicability of the method, we propose the resulting selection strategies for some of the most representative state-of-the-art MOEAs and show numerical results on several benchmark problems. The results demonstrate that the consideration of the Variation Rate can greatly enhance the diversity in decision space for all considered algorithms and problems without a significant loss in the approximation qualities in objective space.

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