One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is acc...

Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in power-voltage characteristics PV cells, caused by uneven distribution solar irradiance on module surface, known as global and local maximum power point (GMPP LMPP). In this paper, new technique for achieving GMPP based dandelion optimizer (DO) algorithm...

Artificial Intelligence (AI) techniques are used for solving the intractable engineering problems. In this study, it is aimed to study the application of artificial bee colony algorithm for predicting the performance of circular diamond saw in sawing of hard rocks. For this purpose, varieties of fourteen types of hard rocks were cut in laboratory using a cutting rig at 5 mm depth of cut, 40 cm/...

In this paper, a new approach based on the Honey bee mating optimization (HBMO) technique is proposed to tune the parameters of the multi-machine power system stabilizers (PSSs). The honey-bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. The PSSs parameters tuning problem ...

Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exp...

In this paper, a cardinality constrained mean-variance model is introduced for the portfolio optimization problems. This model is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. The use of heuristic algorithms in this case is necessary. Some studies have investigated the cardinality constrained mean-variance model using heuristic algorithm. But alm...

Artificial bee colony (ABC) algorithm has been introduced for solving numerical optimization problems, inspired collective behavior of honey bee colonies. ABC algorithm has three phases named as employed bee, onlooker bee and scout bee. In the model of ABC, only one design parameter of the optimization problem is updated by the artificial bees at the ABC phases by using interaction in the bees....

In this paper we compare the performance of Minton’s et al. min-conflicts hill-climbing (MCHC) algorithm [8], Wallace and Freuder’s MCHC algorithm [18], Morris’ breakout algorithm (BA) [9, 10], and modified variants of the BA that we developed on static constraint satisfaction problems and recurrent dynamic constraint satisfaction problems (CSPs & rDCSPs). In this study, our results show that t...