in other words tradeoffs between accuracy ,performance( such as battery life ) and latency
Rudra Tiwari
Pareto Optimality: Pareto optimality is a method that finds the set of non-dominated solutions, in which no other solution can improve one objective without degrading at least one other objective. Weighted Sum Method: This method converts the multi-objective optimization problem into a single objective problem by assigning weights to each objective and summing them. Goal Programming: This method allows the user to specify a set of goals or constraints for each objective and finds the best solution that satisfies them. Evolutionary Algorithms: Evolutionary algorithms such as genetic algorithms and particle swarm optimization are well-suited to solving multi-objective optimization problems because they can efficiently explore the solution space and find a set of non-dominated solutions. Multi-Objective Evolutionary Algorithms (MOEA): MOEAs are specialized algorithms that are designed specifically to solve multi-objective optimization problems. Examples include: Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA2), and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). Hybrid Algorithms: Hybrid algorithms combine two or more of the above methods to improve their effectiveness.
There are several methods for solving or dealing with multi-objective optimization problems in cloud computing architecture, some of which include:
It's worth noting that the best approach for a specific problem will depend on the characteristics of the problem and the resources available. Additionally, it's important to keep in mind that multi-objective optimization problems can have multiple solutions and often require trade-offs between objectives.
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