In view of shortcomings of the particle swarm optimization algorithm such as poor late optimization ability and proneness to local optimization etc, this paper proposes an opposition-based learning particle swarm optimization (OBLPSO) algorithm for the optimization of logistics distribution routes, firstly, establishes a logistics distribution route optimization mathematical model, and then sol...

Automatic plate recognition of vehicles is of great importance in route management systems. Especially that such systems require real-time algorithms to perform the plate recognition task as soon as possible. In this paper, a new plate recognition system based on a fuzzy noise removal technique and the opposition-based micro-differential evolution (OMDE) algorithms is presented. Since populatio...

the present study intended to look into and compare the possible effects of competitive team-based learning (ctbl) with learning together (lt) or cooperative group-based learning (cgbl) – the most popular method of cooperative learning (cl) -- on oral performance of iranian efl intermediate students. after administering the oral interview, this researcher selected a group of 40 almost homogeneo...

Simon Rippon has recently argued against kidney markets on the grounds that introducing the option to vend will result in many people, especially the poor, being subject to harmful pressure to vend. Though compelling, Rippon's argument fails. What he takes to be a single phenomenon-social and legal pressure to vend-is actually two. Only one of these forms of pressure is, by Rippon's own account...

Multi-Objective Optimization (MOO) metaheuristics are commonly used for solving complex MOO problems characterized by non-convexity, multimodality, mixed-types variables, non-linearity, and other complexities. However, often metaheuristics suffer from slow convergence. Opposition-Based Learning (OBL) has been successfully used in the past for acceleration of single-objective metaheuristics. The...

The artificial rabbits optimization (ARO) algorithm is a recently developed metaheuristic (MH) method motivated by the survival strategies of with bilateral symmetry in nature. Although ARO shows competitive performance compared popular MH algorithms, it still has poor convergence accuracy and problem getting stuck local solutions. In order to eliminate effects these deficiencies, this paper de...

– In this article, Teaching-learning opposition based optimization (TLOBO) algorithm based on the natural phenomenon of teaching and learning is applied to design an optimal higher order stable low pass (LP) and high pass (HP) IIR digital filter using different design criterion. The original TeachingLearning Based Optimization (TLBO) algorithm has been remodeled by merging the concept of opposi...

background: according to the available evidence and experiments, problem-based learning is one of the most successful methods to achieve higher educational objectives. in this method, the discussion about the medical sybjects to be learned by the students is based on a real clinical case and participation of the students. various advantages and disadvantages of this method have been addressed i...