The performance of a photovoltaic (PV) array depends on temperature, radiation, shading and load size. Conventional maximum power point tracking (MPPT) methods have acceptable efficiencies under uniform conditions (irradiance = 1000 W/m 2 and temprature = 25 °C), but in dynamic weather conditions, load changes, and also in partial shading conditions due to the presence of several local maximum power points (MPP) in the P-V characteristic, the conventional tracking method does not work well in finding the main MPP. To extract maximum power in all conditions, many algorithms have been proposed, all of which have limitations in terms of convergence speed, output power ripple and efficiency. This research proposes an optimized Fuzzy Logic Controller (FLC) based on the Cuckoo Optimization Algorithm (COA) for MPPT under uniform conditions, dynamic weather conditions, partial shading and under load changes. Finally, the research compared the simulation results with four other popular methods. According to the simulation observations and the result, COA-FLC overcomes the mentioned limitations such as low convergence speed, output power ripple and low tracking efficiency in all conditions. Simulations are performed with MATLAB / Simulink software.

There is a relatively small amount of research covering urban freight movements. Most research dealing with the subject of urban mobility focuses on passenger vehicles, not commercial vehicles hauling freight. However, in many ways, urban freight transport contributes to congestion, air pollution, noise , accident and more fuel consumption which raises logistic costs, and hence the price of products. The main focus of this paper is to propose a new solution for congestion in order to improve the distribution process of goods in urban areas and optimize transportation cost, time of delivery, fuel consumption, and environmental impact, while guaranteeing the safety of goods and passengers. A novel technique for personalization in itinerary search based on city logistics ontology and rules is proposed to overcome this problem. The integration of personalization plays a key role in capturing or inferring the needs of each stakeholder (user), and then satisfying these needs in a given context. The proposed approach is implemented to an itinerary search problem for freight transportation in urban areas to demonstrate its ability in facilitating intelligent decision support by retrieving the best itinerary that satisfies the most users' preferences (stakeholders).

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