abstract: in this paper, a new population-based search called the bees algorithm (ba) is presented to estimate the time-dependent heat transfer coefficient and the corresponding heat flux at the boundaries of a two-dimensional body subjected to transient heat conduction, using the temperature measurements at discrete nodal locations on the boundaries, where heat flux is specified as the boundar...

In a distributed system it can occur that some nodes are idle or lightly loaded while others are heavily loaded. This leads to the opportunity of improving the performance of a distributed system as a whole by remote execution and migrating jobs from heavily-loaded nodes to idle or lightly-loaded nodes. It is the task of the distributed scheduler to schedule processes to nodes (processors) in s...

Artificial bee colony (ABC), an optimization algorithm is a recent addition to the family of population based search algorithm. ABC has taken its inspiration from the collective intelligent foraging behavior of honey bees. In this study we have incorporated golden section search mechanism in the structure of basic ABC to improve the global convergence and prevent to stick on a local solution. T...

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collec...

This Chapter summarizes some of the well known stigmergic computational techniques inspired by nature, mainly for optimization problems developed by mimicking social insects’ behavior. Some facts about social insects namely ants, bees and termites are presented with an emphasis on how they could interact and self organize for solving real world problems. We focused on ant colony optimization al...

Bees Algorithm is one of the optimization algorithms inspired from the natural foraging ways of the honey bees of finding the best solution. It is an iteration algorithm; therefore, it is suffering from slow convergence. The other downside of the Bee Algorithm is that it has needless computation. This means that it spends a long time for the bees algorithm converge the optimum solution. In this...

Renewable energy sources are developed worldwide, owing to high oil prices and in order to limit greenhouse gas emissions. The objective of this research was to study the feasibility of biodiesel production from mountain almond (Prunus Scoparia) oil using ultrasonic system and optimization of the process using Artificial Bees Colony (ABC) Algorithm. The results showed that by increasing the mol...

In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic population), Transgenic Algorithm (transfers of genetic material to another cell that is not descending); (b) Meta-heuristics based on interactions among individual in...

To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. T...