assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. sspco optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. one of the things that smart algorithms are applied to solve is the problem ...

We developed a mathematical model and an algorithm for numerical treatment of a model of honeycomb construction in a beehive. The model contains essential features of the bee-bee and bee-wax interactions, and in a qualitative way captures the dynamics of parallel comb construction. The construction is represented by a set of dynamical coupled partial differential equations for the density of be...

Designing the fuzzy controllers by using evolutionary algorithms and reinforcement learning is an important subject to control the robots. In the present article, some methods to solve reinforcement fuzzy control problems are studied. All these methods have been established by combining Fuzzy-Q Learning with an optimization algorithm. These algorithms include the Ant colony, Bee Colony and Arti...

The paper presents a new algorithm for circuit partitioning that exploits some of the latest innovations of evolutionary approaches. The proposed algorithm based on swarm intelligence technique hybrids discrete version of artificial bee colony algorithm with Inver over operator along with simulated annealing technique for local improvement of solution. The algorithm is tested on small UCLA inst...

It is one of the topics that have been studied extensively on maximum power point tracking (MPPT) recently. Traditional or soft computing methods are used for MPPT. Since approaches more effective than traditional approaches, studies MPPT shifted in this direction. This study aims comparison performance seven meta-heuristic training algorithms neuro-fuzzy The particle swarm optimization (PSO), ...

Clustering is the unsupervised learning in which the data is divided into similar groups (cluster) without any prior knowledge. The emerging swarm-based algorithms become an alternative to the conventional clustering methods to enhance the quality of results. Artificial Bee Colony (ABC) Algorithm is one of the Swarm Intelligent based optimization algorithm that exhibit foraging properties of a ...

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. The Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior ...

The idea behind this work is to solve the Shortest Common Supersequence by using Artificial Bee Colony (ABC) Algorithm. This algorithm is considered as one of the newest nature-inspired swarm-based optimization algorithms and has a promising performance [6]. Shortest Common Supersequence is a classical problem in the field of strings and it is classified as NP-Hard problem [12]. Many algorithms...

K-means algorithm is sensitive to initial cluster centers and its solutions are apt to be trapped in local optimums. In order to solve these problems, we propose an optimized artificial bee colony algorithm for clustering. The proposed method first obtains optimized sources by improving the selection of the initial clustering centers; then, uses a novel dynamic local optimization strategy utili...

In order to resolve the multi-parameter optimization problem of finite impulse response (FIR) digital filter design, a quantum bee colony algorithm (QBCA) is proposed. The proposed QBCA is a multi-dimensional search algorithm for optimization of real numbers, and uses mechanisms of quantum evolution to update the positions of different quantum bees. Computer simulations have showed that FIR dig...