کتب علوم الحدیث میں امثال فقہیہ پر اختلاف مسالک کے اثرات؛ ایک تحقیقی جائزہ The effects of differences of Masalik on the Jurisprudential proverbs stated in the books of hadith studies

The foundation of the Sharia is revelation, revelation is the name of two things, the Qur'an and the Sunnah, since both are related to the news, and to convey the news to others, narrators are needed, so for the propagation of the Qur'an and the Sunnah to future generations. It was necessary to have narrators, the narrators of the Holy Qur'an are called Qira, the narrators of the Sunnah are called Muhaddith, the traditions of the Holy Qur'an are called 'Qara'at' and the traditions of the Sunnah are called 'Ahadith'. Both the Qur'an and the Sunnah are revelations, but still there are some differences between them which are explained in detail in the Book of Principles. It was a difficult task, and the significant efforts made by the Muhadditheen in this regard were more famous and campaigned than the knowledge of al-Qaraat and recitation. He became famous with this, and some people even got the wrong impression that he had nothing to do with jurisprudence, and this wrong impression was reinforced by the behavior of the some Narrators. In reasoning and deriving from the Sunnah, there were many disorders and factors that gave birth to different schools of jurisprudence. For example, a hadith revealed to an imam or a jurist during reasoning has a hidden reason that is not revealed to anyone else. Therefore, there is a difference in argumentation. Similarly, sometimes the hadeeth is correct in a certain issue in front of a jurist, while on the other hand, it is weak in the opinion of another, which leads to diversity in argumentation. When the jurists differed in the derivation of the issues and rulings, in fact, these are cases of priority and non-priority, in which there is, however, scope that any position can be declared preferred based on arguments. Keywords:               Hadith, Muhaddithin, Jurisprudential Proverbs, School of Thoughts, Differences.

Optimization of Route Planning for Dynamic Environments Using Swarm Intelligence

Environments for algorithms can be categorized as static or dynamic. A static environment remains stationary throughout the execution of the algorithm, while in a dynamic environment the environment changes during the execution of the algorithm. The algorithms for planning in static and dynamic environments can be divided into offline and online algorithms. This research implements an online algorithm for an unknown environment and combined exploration and planning in a hybrid architecture. A simulated system of agents based on swarm intelligence is presented for route optimization and exploration. Two versions of the system are implemented and compared for performance- i.e., a simulated ant agent system and a simulated niche based particle swarm optimization. A simulated ant agent system is presented to address the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using a modified ant colony optimization algorithm for dealing with online route planning. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints, and its efficiency has been tested in a mine field simulation with different environment configurations. It is capable of tracking a stationary as well as a non-stationary goal and performs equally well as compared to moving target search algorithm. Route planning for dynamic environment is further extended by using another optimization technique for generation of multiple routes. Simulated niche based particle swarm has been used for dynamic online route planning, optimization of the routes, and it has proved to be an effective technique. It efficiently deals with route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated niche based particle swarm optimization (SN-PSO) is proposed using a modified particle swarm optimization algorithm for dealing with online route planning. The SN-PSO generates and optimizes multiple routes in complex and large environments with constraints. The SN-PSO is shown to be an efficient technique for providing safe, short,and feasible routes under dynamic constraints. The efficiency of the SN-PSO is tested in a mine field simulation with different environment configuration, and it successfully generates multiple feasible routes. Finally, the swarm based techniques are further compared with an evolutionary algorithm (genetic algorithm) for performance and scalability. Statistical results showed that evolutionary techniques perform well in less cluttered environments and their performance degrades with the increase in environment complexity. For small size maps, the evolutionary technique performs well but its efficiency decreases with an increase in map size.