This paper examines the Pareto front of a simple fossil fuel power plant using a common third-order model. This front is first examined analytically. Then the power plant model is transferred over to a steady-state mo...
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ISBN:
(纸本)9781424419050
This paper examines the Pareto front of a simple fossil fuel power plant using a common third-order model. This front is first examined analytically. Then the power plant model is transferred over to a steady-state model using a static neural network and the front is estimated using various geometric and heuristic approaches. This paper is the first of the two stages to eliminate the need for a human to construct a single objective cost function from multiple objectives for use in the power plant optimization. To generate the Pareto front of the power plant, three different optimization techniques are explored, normal-boundary intersection utilizing differential equation, multi-objective particle swarm optimization, and multi-objective evolutionary algorithm optimization.
The Fuzzy Logic Congestion Detection (FLCD) algorithm is a recent proposal for congestion detection in IP networks which combines the good characteristics of both traditional Active Queue Management (AQM) algorithms a...
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The Fuzzy Logic Congestion Detection (FLCD) algorithm is a recent proposal for congestion detection in IP networks which combines the good characteristics of both traditional Active Queue Management (AQM) algorithms and fuzzy logic based AQM algorithms. The Membership Functions (MFs) of the FLCD algorithm are designed using a multi-objective particle swarm optimization (MOPSO) algorithm, in order to achieve optimal performance on all the major performance metrics of IP congestion control. The FLCD algorithm achieves better performance when compared to the basic Fuzzy Logic AQM and Random Explicit Marking (REM) algorithms. Since the optimization process is undertaken offline and is based on a single optimization script, the performance of the FLCD algorithm may not be optimal under different network conditions, due to the fact that the IP environment is characterized by dynamic traffic patterns. This paper proposes two online self-learning and organization structures that enable the FLCD algorithm to learn the system conditions and adjust the fuzzy rule base in accordance with prevailing conditions. The self-organized FLCD algorithm is compared with the unorganized FLCD, the basic Fuzzy Logic AQM and the Adaptive Random Early Detection (RED) algorithms using simulations with dynamic traffic patterns. Performance results show that the self-orginized FLCD algorithm is more robust than the other algorithms. Compared to the unorganized FLCD, the new scheme improves the UDP traffic delay for short round trip times and also reduces packet loss rates. In terms of jitter, fairness and link utilization, it exhibits a similar performance to the unorganized FLCD algorithm.
In this paper, multi-objective particle swarm optimization (MOPSO) Algorithm is used to find the optimal location of Thyristor Controlled Series Compensator (TCSC) and its parameter in order to increase Total Transfer...
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ISBN:
(纸本)9781424412969
In this paper, multi-objective particle swarm optimization (MOPSO) Algorithm is used to find the optimal location of Thyristor Controlled Series Compensator (TCSC) and its parameter in order to increase Total Transfer Capability(TTC), reduce total transmission losses and reduce voltage deviation. This multi-objectiveoptimization problem is solved by using the MOPSO with sigma method and encouraging results are obtained.
The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will ...
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The pylon structure of an airplane is very complex, and its high-fidelity analysis is quite time-consuming. If posterior preference optimization algorithm is used to solve this problem, the huge time consumption will be unacceptable in engineering practice due to the large amount of evaluation needed for the algorithm. So, a new interactive optimization algorithm-interactive multi-objective particle swarm optimization （IMOPSO） is presented. IMOPSO is efficient, simple and operable. The decision-maker can expediently determine the accurate preference in IMOPSO. IMOPSO is used to perform the pylon structure optimization design of an airplane, and a satisfactory design is achieved after only 12 generations of IMOPSO evolutions. Compared with original design, the maximum displacement of the satisfactory design is reduced, and the mass of the satisfactory design is decreased for 22%.
In this paper, multi-objective particle swarm optimization (MOPSO) Algorithm is used to find the optimal location of Thyristor Controlled Series Compensator (TCSC) and its parameter in order to increase Total Transfer...
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In this paper, multi-objective particle swarm optimization (MOPSO) Algorithm is used to find the optimal location of Thyristor Controlled Series Compensator (TCSC) and its parameter in order to increase Total Transfer Capability(TTC), reduce total transmission losses and reduce voltage deviation. This multi-objectiveoptimization problem is solved by using the MOPSO with sigma method and encouraging results are obtained.
A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrai...
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A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objectiveoptimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.
Tato dizertační práce se zabývá vývojem optimalizačního nástroje, který je založen na metodě particleswarmoptimization a je poté aplikován na dva typy oběžnýc...
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Tato dizertační práce se zabývá vývojem optimalizačního nástroje, který je založen na metodě particleswarmoptimization a je poté aplikován na dva typy oběžných kol radiálních čerpadel.
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