Diego Luis de Andrade Bernert

A chaotic firefly algorithm applied to reliability-redundancy optimization

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA is a stochastic metaheuristic approach based on the idealized behavior of the flashing characteristics of fireflies. In FA, the flashing light can be formulated in such a way that it is associated with the objective function to be optimized, which makes it possible to formulate the firefly algorithm. This paper introduces a modified FA approach combined with chaotic sequences (FAC) applied to reliability-redundancy optimization. In this context, an example of mixed integer programming in reliability-redundancy design of an overspeed protection system for a gas turbine is evaluated. In this application domain, FAC was found to outperform the previously best-known solutions available.

Chaotic differential Harmony Search algorithm applied to power economic dispatch of generators with multiple fuel options

The Harmony Search (HS) algorithm was originally conceptualized using the musical improvisation process of searching for a perfect state of harmony. The HS algorithm uses a random search, which is based on random selection, memory consideration, and pitch adjusting. This paper proposes a modified HS approach combined with differential evolution and chaotic sequences to solve the economic load dispatch problem of thermal generators with the valve-point effect. The proposed modified HS algorithm was validated in a power economic problem comprised by 10 generating units with valve-point effects and multiple fuels for a load demand of 2500 MW. Simulation results and performance analysis show that the modified HS algorithm presented promising results when compared with results of other optimization methods reported in recent literature.

A Harmony Search Approach Using Exponential Probability Distribution Applied to Fuzzy Logic Control Optimization

Fuzzy logic control (FLC) systems have been investigated in many technical and industrial applications as a powerful modeling tool that can cope with the uncertainties and nonlinearities of modern control systems. However, a drawback of FLC methodologies in the industrial environment is the number of tuning parameters to be selected. In this context, a broad class of meta-heuristics has been developed for optimization tasks. Recently, a meta-heuristic called harmony search (HS) algorithm has emerged. HS was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. Inspired by the HS optimization method, this work presents an improved HS (IHS) approach using exponential probability distribution to optimize the design parameters of a FLC with fuzzy PI (proportional-integral) plus derivative action conception. Numerical results presented here indicate that validated FLC design with IHS tuning is effective for the control of a pH neutralization nonlinear process.

Reliability-Redundancy Optimization Using a Chaotic Differential Harmony Search Algorithm

In many industrial systems, reliability has been considered as an important design measure. In this context, the system reliability maximization subject to performance and cost constraints is well known as reliability optimization problem. The diversity of system structures, resource constraints, and options for reliability improvement has led to the construction and analysis of several optimization methods, such as dynamic programming, Lagrangian multiplier, and heuristic approaches. On the other hand, a broad class of metaheuristics has been developed for reliability-redundancy optimization. Metaheuristics can overcome many limitations of classical optimization methods and offer a practical way to solve complex optimization problems in reliability engineering. Our study considers one of the latest metaheuristics, harmony search (HS), to solve the reliability-redundancy optimization problems. The HS algorithm was originally inspired by the improvisation process of Jazz musicians. The HS algorithm uses a random search, which is based on random selection, memory consideration, and pitch adjusting. The purpose of this study is to introduce a modified HS approach combined with differential evolution and chaotic sequences to solve optimization problems in reliability engineering. The validity and efficiency of the proposed HS approach are evaluated in two benchmark problems (an overspeed protection system for a gas turbine and a series-parallel system) of mixed integer programming in reliability-redundancy design. Simulation results show that the proposed HS produces promising results in comparison to other optimization methods available in the recent literature for mentioned benchmark problems.

A harmony search algorithm combined with differential operator applied to reliability-redundancy optimization

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony search (HS) algorithm has emerged. HS was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. This paper introduces a modified HS approach combined with an operator of differential evolution — a paradigm of evolutionary computation — to solve optimization problems in reliability engineering. In this context, an example of mixed integer programming in reliability-redundancy design of an over-speed protection system for a gas turbine is evaluated. In this application domain, HS was found to outperform the previously best-known solutions available.