Gradient-based parameter optimization is commonly used for training neural networks and optimizing the performance of other complex systems that only contain continuously differentiable functions. However, there is a large class of important parameter optimization problems involving systems containing discretevalued functions that do not permit the direct use of gradient-based methods. Examples...

nowadays, due to inherent complexity of real optimization problems, it has always been a challenging issue to develop a solution algorithm to these problems. single row facility layout problem (srflp) is a np-hard problem of arranging a number of rectangular facilities with varying length on one side of a straight line with aim of minimizing the weighted sum of the distance between all facility...

Roughly speaking, an optimization problem has the following outline: given an instance of the problem, find the “best” solution among all solutions to the given instance. We will be mostly interested in discrete optimization problems where the instances and the solution set for each instance is from a discrete set. This is in contrast to continuous optimization where the input instance and the ...

Genetic Algorithms (GAs) have been successful in global optimization problems, optimal control, pattern recognition, resource allocation and others. The use of GAs was introduced for the optimal control of discrete time system. After transforming optimal control problem into unconstrained optimization one with respect to control variables, we utilized GAs to solve this optimization problem, and...

The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An algorithm for solving the latter optimization problem is developed which allows one to significantly reduce the computational efforts. This algorithm is based on the so-called discrete gradient method. Results of numerical experiments are presented which demonstrate the effectiveness of the propo...

Design a new novel intelligence algorithm which is called as chaotic quantum bee colony optimization (CQBCO) for discrete optimization problem. The proposed CQBCO applies the chaotic theory to quantum bee colony optimization (QBCO), which is an effective discrete optimization algorithm. Then the proposed chaotic quantum bee colony algorithm is used to solve benchmark functions and optimization ...

Bat algorithm (BA) is one of the most recent bioinspired algorithms. It is based on the echolocation behavior of microbats. The standard BA is proposed only for continuous optimization problems. In this paper, a binary bat algorithm has been developed and implemented to solve the graph coloring problem. To show the feasibility and the effectiveness of the algorithms, we have used the DIMACS ben...

Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. The various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different fields. In this paper a new binary version of the ABC algorithm inspired by quantum computing, c...