النزعة الصوفية في شعر خوشحال خان

This article focuses on the various aspects of Khushal's mystical poetry. Khushal was well-read and had a lot of scholarly exposure. He also spent time in the company of great scholars. All these factors contributed to his mystical poetry. I. According to him the servant of God knows himself. In other words those people know themselves who know Allah. Ii This world becomes a mirage for those who believes in Allah and the world seems a useless place to them. Iii. There is a universe in the heart of a “darwesh”. Iv. There are two types of mind; one is worldly and the other is spiritual. They both consider each other as the same. V. True love does not depend on wisdom but it depends on the passion of the individuals. In short, Khushal khan khattak has expressed great mystical thoughts in his poetry for the benefits of all and sundry, and invites them to think about their near future and see what is happening around them and what will be the answer of that questions which would be asked on the day of resurrection.

Combining Pso Algorithm and Honey Bee Food Foraging Behavior for Solving Multimodal and Dynamic Optimization Problems

stract Swarm intelligence algorithms are taking the spotlight in the field of function optimization. In this research our attention centers on combining the Particle Swarm Optimization (PSO) algorithm with food foraging behavior of honey bees. The resulting algorithm (called HBF-PSO) and its variants are suitable for solving multimodal and dynamic optimization problems. We focus on the niching and speciation capabilities of these algorithms which allow them to locate and track multiple peaks in environments which are multimodal and dynamic in nature. The HBF-PSO algorithm performs a collective foraging for fitness in promising neighborhoods in combination with individual scouting searches in other areas. The strength of the algorithm lies in its continuous monitoring of the whole scouting and foraging process with dynamic relocation of the bees (solution/particles) if more promising regions are found. We also propose variants of the algorithm in which each bee has a different position update equation and we utilize genetic programming (GP) for continuous evolution of these position update equations. This process ensures adaptability and diversity in the swarm which leads to faster convergence and helps to avoid premature convergence. We also explore the use of opposite numbers in our algorithm and incorporate opposition based initialization, opposition based generation jumping and opposition based velocity calculation. The proposed algorithm and its variants are tested on a suite of benchmark optimization problems. In the final portion of our work we report our experiments on the training of feedforward neural networks utilizing our proposed algorithms.