Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contributio...

Design optimization of cable-stayed bridges is a challenging optimization problem because a large number of variables is usually involved in the optimization process. For these structures the design variables are cross-sectional areas of the cables. In this study, an efficient metaheuristic algorithm namely, momentum search algorithm (MSA) is used to optimize the design of cable-stayed bridges....

Discrete-event simulation based optimization is the process of finding the optimum design of a stochastic system when the performance measure(s) could only be estimated via simulation. Randomness in simulation outputs often challenges the correct selection of the optimum. We propose an algorithm that merges Ranking and Selection procedures with a large class of random search methods for continu...

In this chapter, we consider the issue of Hidden Markov Model (HMM) training. First, HMMs are introduced and then we focus on the particular HMMs training problem. We emphasize the difficulty of this problem and present various criteria that can be considered. Many different adaptations of metaheuristics have already been used but, until now, a few extensive comparisons have been performed on t...

This paper deals with discrete-event simulation based on optimization of a catchment basin management. The integration of optimization techniques into modeling and simulation relies on the evolution of the studied model using decisions based on previous simulation results. Three different categories of optimization via simulationmethods can be found in the literature: rank-and-selection methods...

The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication. The first part of the paper is devoted to the detailed description of the existing algorithm. Then some suggestions for extending the si...

a model for missing data in mixed binary and continuous responses, which can be used on cross-sectional data, is presented. in this model response indicator for the binary response can be dependent on the continuous response. a closed form for the likelihood is found. for data with a complicated pattern of missing responses some new residuals are also proposed. the model of multiplicative heter...

Reduction of Single Input Single Output (SISO) discrete systems into lower order model, using a conventional and an evolutionary technique is presented in this paper. In the conventional technique, the mixed advantages of Modified Cauer Form (MCF) and differentiation are used. In this method the original discrete system is, first, converted into equivalent continuous system by applying bilinear...

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...

In combinatorial optimization, near-optimal algorithms such as metaheuristics allow to iteratively solve in a reasonable time NP-hard complex problems. Two main categories of metaheuristics are distinguished: population-based metaheuristics (P-metaheuristics) and solution-based metaheuristics (S-metaheuristics). P-metaheuristics are population-oriented as they manage a whole population of solut...