As an optimization algorithm that imitates the natural selection and genetic mechanism, genetic algorithm applied in the combination with the computer technology, gradually obtained rapid development along with the co...
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In this research work, a renewable and clean energy system comprising of wind power, solar power and battery storage is designed with the primary objective of optimizing the total annual operating cost. Artificial Hum...
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A new improved algorithm (IGWO) is proposed based on the Grey Wolf optimization (GWO) algorithm to solve the issue of low overall coverage easily caused by the random deployment of nodes in wireless sensor networks. T...
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Following the successful use of Propositional Satisfiability (SAT) algorithms in Boolean optimization (e.g., Maximum Satisfiability), several SAT-based algorithms have been proposed for Multi-Objective Combinatorial O...
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To address the problem, we need to deploy two edge servers in a specific area to maximize the computational demand of the coverage. The problem can be transformed into a maximum coverage problem where the goal is to s...
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Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when sea...
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In recent years, many constrained multi-objective evolutionary algorithms have been proposed to address com-plex constrained multi-objective optimization problems and have shown significant performance. However, when ...
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Aiming at the weaknesses of Particle Swarm optimization algorithm (PSO) such as the easiness to get trapped into local optimal, slow convergence speed, and low precision, an improved PSO algorithm called Selectivity-P...
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Photovoltaic (PV) systems help reduce modern climatic change by reducing the harmful emissions raised when using fossil fuels. If all the PV modules in the string are subjected to uniform sun radiation, the PV system&...
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We propose a method that achieves near-optimal rates for smooth stochastic convex optimization and requires essentially no prior knowledge of problem parameters. This improves on prior work which requires knowing at l...
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