A new population-based search algorithm called the Bees Algorithm (BA) is presented in this paper. The algorithm mimics the food foraging behavior of swarms of honey bees. This algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization and with good numerical optimization results. ABC is a meta-heu...

Infinite population models are important tools for studying population dynamics of evolutionary algorithms. They describe how the distributions of populations change between consecutive generations. In general, infinite population models are derived from Markov chains by exploiting symmetries between individuals in the population and analyzing the limit as the population size goes to infinity. ...

An efficient methodology is proposed to find optimal shape of arch dams on the basis of constrained natural frequencies. The optimization is carried out by virtual sub population (VSP) evolutionary algorithm employing real values of design variables. In order to reduce the computational cost of the optimization process, the arch dam natural frequencies are predicted by properly trained back pro...

job shop scheduling problem has significant importance in many researchingfields such as production management and programming and also combinedoptimizing. job shop scheduling problem includes two sub-problems: machineassignment and sequence operation performing. in this paper combination ofparticle swarm optimization algorithm (pso) and gravitational search algorithm(gsa) have been presented f...

this study is focused on developing an integrated optimization-simulation based genetic algorithm model (iosga) to develop the operational policies for a multi-purpose reservoir system. the objective function of the optimization model is considered to be a linear function of reliability (rel), resiliency (res), and vulnerability (vul) of the river-reservoir system. genetic algorithm (ga) is emp...

designing of a pid controller is a very common method for industrial process control and due to its very simple and efficient function; it is used in a wide variety of industrial applications. pid controller to reduce the steady state error and dynamic response of the system is used. pid controller design is an inevitable problem in setting the coefficients need to try a lot of trial and error,...

It is difficult for the existing multi-population genetic algorithms with space partition to be successfully applied to multi-objective optimization problems. Multi-population genetic algorithms with space partition for multi-objective optimization problems are designed in this paper in allusion to the characteristics of multi-objective optimization problems. A complicated optimization problem ...

In order to overcome the drawbacks of falling into local extremum and lower optimization precision of standard particle swarm optimization (PSO) algorithm, multipopulation strategy, adaptive dynamic adjustment strategy and co-evolution mode are introduced into the standard PSO algorithm in order to propose an improved coevolutionary PSO(MPACEPSO) algorithm based on multi-strategy evolution mode...

The population migration algorithm is a very effective evolutionary algorithm for solving single-objective optimization problems, but very few applications are available for solving multi-objective optimization problems (MOPs). The current study proposes an improved population migration algorithm for solving MOPs based on the vector evaluated method and the dynamic weighted aggregation. The loc...

the multiple traveling salesman problem (mtsp) is a generalization of the famous traveling salesman problem (tsp), where more than one salesman is used in the solution. although the mtsp is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. this paper presents an efficient and evolutionary optimization algorith...