real-time multi-objective optimization problems
Mean VaR portfolio optimization: a nonparametric approach
... Portfolio optimization involves the optimal assignment of limited capital to dif- ferent available financial assets to achieve a reasonable trade-off between profit and ...portfolio optimization in the ...
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Optimization of Time, Cost, and Quality in Critical Chain Method Using Simulated Annealing (RESEARCH NOTE)
... completion time, uncertainty and ambiguity of plans should be taken into consideration in assessing buffer size ...in multi-project environments could achieve reliable and on-time delivery within a ...
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AHP COA Combined Algorithm for Selecting a Digital Production Machine Design
... more real engineering problems in a multi-objective optimization, maximizing performance increasing reliability, reducing costs, ...processing time, at the same ...the ...
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The relationship between multi-objective robustness concepts and set-valued optimization
... in multi-objective optimization problems is very important in many ...most real world optimization problems are contam- inated with uncertain data, especially traffic ...
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A Multi objective QoS Optimization with Fuzzy Based Parameter Setting for Real Time Multicasting
... Multi-objective optimization is used to solve optimiza- tion problems that have two or more number of conflict- ing objectives, where there may not exist an unique op- timal ...all ...
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A multi objective hyper heuristic based on choice function
... first time, is employed as a (high level heuristic) selection mechanism to deal with the multi-objective optimization ...our multi-objective hyper-heuristic framework, a learning ...
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Immune Optimization Approach for Dynamic Constrained Multi Objective Multimodal Optimization Problems
... In real-world engineering problems, a great number of optimization problems often involve in multiple time- varying multimodal sub-objectives and constraints, such as portfolio ...
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Solving multiobjective constrained trajectory optimization problem by an extended evolutionary algorithm
... trajectory optimization prob- lems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several ...the ...
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
... colony optimization, particle swarm optimization, bee-inspired algorithms, bac- terial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be ...
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An Evolutionary Multi-Objective Optimization Framework for Bi-level Problems
... densely populated regions. It lowers each population member’s fitness by an amount nearly equal to the number of similar individuals in the population. In other words, solutions are penalized if there are too many ...
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Review of Multi-criteria Optimization Methods – Theory and Applications
... Multi-objective optimization originally grew out of three areas: economic equilibrium and welfare theories, game theory, and pure mathematics (Marler and Arora ...on multi-criteria decision ...
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A Unified Framework for Participation of Responsive End-User Devices of Smart Grid with Imopso
... improved multi-objective problem solved by the particle swarm ...improved multi-objective particle swarm optimization algorithm (IMOPSO) is designed to efficiently solve ...
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UTILITARIAN MECHANISM DESIGN FOR MULTIOBJECTIVE OPTIMIZATION
... Another fundamental technique is the Lagrangian relaxation method. The basic idea is relaxing the budget constraints, and lifting them into the objective function, where they are weighted by Lagrangian ...
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EFFICIENT REQUIREMENT PRIORITIZATION BASED ON ENHANCED MULTI VERSE OPTIMIZER
... hybrid time varying particle swarm optimization and genetic algorithm method (TVPSOGA) was introduced to solve multi-objective reactive power dispatch (MORPD) ...non-linear ...
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The application of multi-objective charged system search algorithm for optimization problems
... A careful scrutiny of Figure 4 indicates that the proposed MOCSS algorithm for nding solutions has outperformed all benchmarks. The answers that are close to the true Pareto front, uniformly dispersed on it, should be ...
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Pareto Optimal Multi-Objective Dynamical Balancing of a Slider-Crank Mechanism Using Differential Evolution Algorithm
... of objective functions of non-superior points were mapped in a range of 0 to ...all objective-functions, the optimal point, which is called D, has the least summation of mapped objective ...four ...
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A new approach on solving Intuitionistic fuzzy linear programming problem
... In this paper, we propose a new approach for solving Intuitionistic Fuzzy Linear Programming Problems (IFLPP) involving triangular intuitionistic fuzzy numbers (TIFN). We introduce a new algorithm for the solution ...
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Utilizing Of Fractional Programming For Multi Objective Multi Item Solid Transportation Problems In Fuzzy Environment
... of the real line, known as fuzzy numbers). For the fuzzy set theory development, we may referee to the papers of Kaufmann, 1975, and Dubois and Prade ,1980, they extended the use of algebraic operations of ...
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An Evolutionary Algorithm for Large-Scale Sparse Multi-Objective Optimization Problems
... multi-objective optimization, training neural network is usually regarded as a bi-objective MOP, i.e., minimizing both training error and model complexity [18]. Therefore, the Pareto optimal ...
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Multi indicators Multi objective Evolutionary Algorithm with Q Learning for Real world Network Optimization
... novel multi-indicator based evolutionary algorithm with reinforcement learning (MIEA-RL) for the RFID network optimization, which can achieve a better performance on the multi-objective RNP ...
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