This paper presents a rigorous running time analysis of evolutionary algorithms on pseudo-Boolean multiobjective optimization problems. We propose and analyze different population-based algorithms, the simple evolutio...
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This paper presents a rigorous running time analysis of evolutionary algorithms on pseudo-Boolean multiobjective optimization problems. We propose and analyze different population-based algorithms, the simple evolutionary multiobjective optimizer (SEMO), and two improved versions, fair evolutionary multiobjective optimizer (FEMO) and greedy evolutionary multiobjective optimizer (GEMO). The analysis is carried out on two biobjective model problems, leading ones trailing zeroes (LOTZ) and count ones count zeroes (COCZ), as well as on the scalable m-objective versions mLOTZ and mCOCZ. Results on the running time of the different population-based algorithms and for an alternative approach, a multistart (1+1)-EA based on the epsilon-constraint method, are derived. The comparison reveals that for many problems, the simple algorithm SEMO is as efficient as this (1+1)-EA. For some problems, the improved variants FEMO and GEMO are provably better. For the analysis;we propose and apply two general tools, an upper bound technique based on a decision space partition and a randomized graph search algorithm, which facilitate the analysis considerably.
A numerical method is proposed for detecting resonances of conservative maps which reduces this task to an optimization problem. We then solve this problem using evolutionary algorithms, which are methods for global o...
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A numerical method is proposed for detecting resonances of conservative maps which reduces this task to an optimization problem. We then solve this problem using evolutionary algorithms, which are methods for global optimization inspired by biological evolution. The proposed methodology is simple and can be easily applied to maps of arbitrary dimensions. In this Letter we apply it to several examples of 2- and 4-dimensional conservative maps, with quite promising results concerning integrability, the location of resonances and the presence of chaotic regions surrounding the island chains that correspond to these resonances. (C) 2008 Elsevier B.V. All rights reserved.
In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography,...
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In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Regarding the solution of the problem, this paper introduces a greedy heuristic algorithm which is able to obtain a reasonable initial solution for the problem. This heuristic is then used to seed the initial population of the evolutionary algorithm, improving its performance. It is shown that the proposed seeded evolutionary approach is able to obtain very good solutions to this problem, which maximize the economical benefit which can be obtained from the wind farm. (C) 2011 Elsevier Ltd. All rights reserved.
Hydropower generation in the Hetch Hetchy Power System is strongly tied to snowmelt dynamics in the central Sierra Nevada and consequently is particularly financially vulnerable to changes in snowpack availability and...
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Hydropower generation in the Hetch Hetchy Power System is strongly tied to snowmelt dynamics in the central Sierra Nevada and consequently is particularly financially vulnerable to changes in snowpack availability and timing. This study explores the Hetchy Hetchy Power System as a representative example from the broader class of financial risk management problems that hold promise in helping utilities such as SFPUC to understand the tradeoffs across portfolios of risk mitigation instruments given uncertainties in snowmelt dynamics. An evolution-ary multi-objective direct policy search (EMODPS) framework is implemented to identify time adaptive stochastic rules that map utility state information and exogenous inputs to optimal annual financial decisions. The resulting financial risk mitigation portfolio planning problem is mathematically difficult due to its high dimensionality and mixture of nonlinear, nonconvex, and discrete objectives. These features add to the difficulty of the problem by yielding a Pareto front of solutions that has a highly disjoint and complex geometry. In this study, we contribute a diagnostic assessment of state-of-the-art multi-objective evolutionary algorithms' (MOEAs') abilities to support a DPS framework for managing financial risk. We perform comprehensive diagnostics on five algorithms: the Borg multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non dominated Sorting Genetic Algorithm III (NSGA-III), Reference Vector Guided evolutionary Algorithm (RVEA), and the Multi-objective evolutionary Algorithm Based on Decomposition (MOEA/D). The MOEAs are evaluated to characterize their controllability (ease-of-use), reliability (probability of success), efficiency (minimizing model evaluations), and effectiveness (high quality tradeoff representations). Our results show that newer decomposition, reference point, and reference vector algorithms are highly sensitive to their parameterizations (difficult
Handoff reduction is considered one of the most exciting challenges in the study of cognitive radio networks. Spectrum handoff occurs between channels when a licensed user needs to access a channel which is currently ...
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Handoff reduction is considered one of the most exciting challenges in the study of cognitive radio networks. Spectrum handoff occurs between channels when a licensed user needs to access a channel which is currently occupied by an unlicensed user. Once the entry of the authorized user has been detected, the secondary user must move to an idle channel. This process continues until the unlicensed user finishes his transmission. This paper addresses the problem of spectrum mobility in a known radio electric environment, guiding secondary users through routes created with bio-inspired algorithms. The authors formulate a spectrum allocation scheme for multiple secondary users using two bio-inspired algorithms. The simulation results indicate that the Max feeding optimization algorithm proposed offers robustness and low complexity, which makes it a solution that is more in line with the spectrum allocation problem in cognitive radio networks.
It remains a challenge to identify a satisfactory set of tradeoff solutions for many-objective optimization problems that have more than three objectives. Coevolving the solutions with preference is becoming increasin...
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It remains a challenge to identify a satisfactory set of tradeoff solutions for many-objective optimization problems that have more than three objectives. Coevolving the solutions with preference is becoming increasingly popular due to the enhanced local search capability, which makes it suitable for solving many-objective optimization problems. The framework of preference-inspired co-evolutionary algorithms (PICEAs) is suitable for obtaining promising performance for such problems, and the PICEA with goal vectors (PICEA-g) has achieved good performance in many applications. In this paper, an improved PICEA-g is proposed to further resolve this long-standing problem. The local principal component analysis operator is used as a controller to further expand the ability of the PICEA-g algorithm and enhance the convergence of PICEA-g. The proposed algorithm was evaluated using several widely used benchmark test suites that had 3-15 objectives and made a systematic comparison with five state-of-the-art multi-objective evolutionary algorithms. The resulting substantial amount of experimental results revealed that the algorithm we proposed could have good performance on most of the test suites assessed in our research, and it performs very well compared with other many-objective optimization algorithms. In addition, a sensitivity test was carried out to explore the impact of a key parameter in the algorithm we proposed in this study.
The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes ...
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The Steiner tree problem (STP) aims to determine some Steiner nodes such that the minimum spanning tree over these Steiner nodes and a given set of special nodes has the minimum weight, which is NP-hard. STP includes several important cases. The Steiner tree problem in graphs (GSTP) is one of them. Many heuristics have been proposed for STP, and some of them have proved to be performance guarantee approximation algorithms for this problem. Since evolutionary algorithms (EAs) are general and popular randomized heuristics, it is significant to investigate the performance of EAs for STP. Several empirical investigations have shown that EAs are efficient for STP. However, up to now, there is no theoretical work on the performance of EAs for STP. In this article, we reveal that the (1+1) EA achieves 3/2-approximation ratio for STP in a special class of quasi-bipartite graphs in expected runtime O(r(r + s - 1) . w(max)), where r, s, and w(max) are, respectively, the number of Steiner nodes, the number of special nodes, and the largest weight among all edges in the input graph. We also show that the (1+1) EA is better than two other heuristics on two GSTP instances, and the (1+1) EA may be inefficient on a constructed GSTP instance.
The combination of traditional evolution strategies and heuristics from expert knowledge leads to the RELOPAT optimization program. In combination with reactor simulation codes-in this investigation the nodal reactor ...
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The combination of traditional evolution strategies and heuristics from expert knowledge leads to the RELOPAT optimization program. In combination with reactor simulation codes-in this investigation the nodal reactor code PRISM of Siemens/KWU - a powerful program system for the design of a numerically optimized pressurized water reactor (PWR) loading pattern was designed. Furthermore, the technic of parallel computing was introduced successfully. Simple parallel algorithmic structures on the level of optimization algorithms, combined with a low amount of communication between processors, allow workstation clusters to be used efficiently. Highly promising results were obtained by comparing recalculations of different known loading patterns for several PWRs of different sizes and varying constraints with solutions based on human expertise. The economic potential of the improvements now leads to a program that meets industrial requirements.
The paper presents the lateral acceleration control design of a missile model using the evolution strategy (ES). The nonlinear fixed-structure-like controller is represented by the singleton fuzzy model. This allows a...
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The paper presents the lateral acceleration control design of a missile model using the evolution strategy (ES). The nonlinear fixed-structure-like controller is represented by the singleton fuzzy model. This allows a variation of shapes of the gain functions to be explored, and subsequent modification is fairly easy. Instead of finding an optimal decomposition of the fuzzy controller, a set of fuzzy rules is unconventionally optimized for the overall gain surfaces that guarantee acceptable performances for the closed-loop system over the whole operating envelope. The simulation results show that the designed fuzzy gain-scheduled controller is a robust tracking controller for all perturbation vertices.
This paper provides a comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms. We review approaches that go from simple variations of a penalty function, to ...
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This paper provides a comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms. We review approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies. Besides describing briefly each of these approaches (or groups of techniques), we provide some criticism regarding their highlights and drawbacks. A small comparative study is also conducted, in order to assess the performance of several penalty-based approaches with respect to a dominance-based technique proposed by the author, and with respect to some mathematical programming approaches. Finally, we provide some guidelines regarding how to select the most appropriate constraint-handling technique for a certain application. and we conclude with some of the most promising paths of future research in this area. (C) 2002 Elsevier Science B.V. All rights reserved.
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