This paper presents a new mathematical model for a redundancyallocation problem (RAP) withcold-standby redundancy strategy and multiple component choices.The applications of the proposed model arecommon in electrical power, transformation,telecommunication systems,etc.Manystudies have concentrated onone type of time-to-failure, butin thispaper, two components of time-to-failures which follow hy...

Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired b...

in this paper, the flexible job shop scheduling problem with machine flexibility and controllable process times is studied. the main idea is that the processing times of operations may be controlled by consumptions of additional resources. the purpose of this paper to find the best trade-off between processing cost and delay cost in order to minimize the total costs. the proposed model, flexibl...

due to the fact that the error surface of adaptive infinite impulse response (iir) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. in this case, global optimization techniques are required in order to avoid the local minima. harmony search (hs), a musical inspired metaheuristic, is a recently ...

In this paper we propose an efficient offline job scheduling algorithm working in a grid environment that is based on a relatively new evolutionary metaheuristic called generalized extremal optimization (GEO). We compare our experimental results with those obtained using a very popular evolutionary metaheuristic, the genetic algorithm (GA). The scheduling algorithm implies two-stage scheduling....

Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more ―standard‖ algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could...

This paper reviews modern genetic algorithm based approaches for solving job shop scheduling problems Genetic algorithms represent one of the most popular and mostly used metaheuristic methods applied for solving many optimization problems within last few decades. Job shop scheduling represents one of the hardest combinatorial optimization problems where number of possible schedules drastically...

This paper proposes a modified sine cosine algorithm (MSCA) for discrete sizing optimization of truss structures. The original sine cosine algorithm (SCA) is a population-based metaheuristic that fluctuates the search agents about the best solution based on sine and cosine functions. The efficiency of the original SCA in solving standard optimization problems of well-known mathematical function...

We present a metaheuristic algorithm for creating heuristic functions for CSP, especially SAT, with as low human interaction as possible. We use the concept of genetic programming to evolve local search heuristic functions encoded as a list of RAM model-like computer instructions. However, we’ve extended the RAM model language to reflect the features of genetic programming and the random mixing...