Fractional Differential Equations

Welding Metallurgy of

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html. A novel multi-objective algorithm called Multi-objective Grey Wolf Optimizer is proposed.MOGWO is benchmarked on 10 challenging multi-objective test problems.The quantitative results show the superior convergence and coverage of MOGWO.The coverage ability of MOGWO is confirmed by the qualitative results as well.

Multi-objective grey wolf optimizer

Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers

Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine

Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system

Feedback linearization control based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid

On-Off control based particle swarm optimization for maximum power point tracking of wind turbine equipped by DFIG connected to the grid with energy storage

In this paper, a fuzzy logic control (FLC) based “Mamdani” is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. As results indicated, the convergence time of maximum power point (MPP) of the proposed algorithm is better than that of the conventional Perturb and Observation (P&O) technique.

Maximum power point tracking using fuzzy logic control for photovoltaic system

Particle swarm optimization based sliding mode control of variable speed wind energy conversion system

Sami Kahla

Papers

Fuzzy-PSO controller design for maximum power point tracking in photovoltaic system

Feedback linearization control based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid

On-Off control based particle swarm optimization for maximum power point tracking of wind turbine equipped by DFIG connected to the grid with energy storage

Maximum power point tracking using fuzzy logic control for photovoltaic system

Particle swarm optimization based sliding mode control of variable speed wind energy conversion system