Heder S. Bernardino

A hybrid genetic algorithm for constrained optimization problems in mechanical engineering

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.

A new hybrid AIS-GA for constrained optimization problems in mechanical engineering

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.

Using performance profiles to analyze the results of the 2006 CEC constrained optimization competition

Performance profiles are an analytical tool for the visualization and interpretation of the results of benchmark experiments. In this paper we discuss their explanatory power, and argue that they should be more widely used by the evolutionary computation community. We also introduce some novel performance measures which can be extracted from the performance profiles. In order to illustrate their potential, we apply the referred profiles to the analysis of the results of the CEC 2006 constrained optimization competition. While some of the results are corroborated, some new facts are pointed out and additional conclusions are drawn.

Ant colony approaches for multiobjective structural optimization problems with a cardinality constraint

Two Ant Colony Optimization algorithms are proposed to tackle multiobjective structural optimization problems with an additional constraint. A cardinality constraint is introduced in order to limit the number of distinct values of the design variables appearing in any candidate solution. Such constraint is directly enforced when an ant builds a candidate solution, while the other mechanical constraints are handled by means of an adaptive penalty method (APM). The test-problems are composed by structural optimization problems with discrete design variables, and the objectives are to minimize both the structures weight and its maximum nodal displacement. The Pareto sets generated in the computational experiments are evaluated by means of performance metrics, and the obtained designs are also compared with solutions available from single-objective studies in the literature.

Artificial Immune Systems for Optimization

Artificial Immune Systems (AISs) are computational methods inspired by the biological immune system and thus classified as a nature-inspired meta-heuristic along with genetic algorithms, ant colony optimization, particle swarm optimization, and others. This chapter is focused on the application of AISs to solve optimization problems. Optimization is a mathematical principle largely applied to design and operational problems in all types of engineering, as well as a tool for formulating and solving inverse problems such as parameter identification in scientific and engineering situations. This chapter provides a survey of the applications of AISs in optimization. The main contributions are studied and contrasted with respect to the different concepts of the biological immune system, such as clonal selection, hypermutation, immune network, affinity, etc. that have inspired such techniques. The main types of optimization problems are considered, namely, (i) unconstrained problems, (ii) constrained problems, (iii) multimodal problems where several optima are to be obtained in a single run, and, finally (iv) multi-objective problems, where an approximation to the Pareto set is to be obtained as the result of a single run of the algorithm. Although AISs are good for solving optimization problems, useful features from other techniques have been combined with a “pure” AIS in order to generate hybrid AIS methods with improved performance.

Surrogate-assisted clonal selection algorithms for expensive optimization problems

Clonal selection algorithms are computational methods inspired by the behavior of the immune system which can be applied to solve optimization problems. However, like other nature inspired algorithms, they can require a large number of objective function evaluations in order to reach a satisfactory solution. When those evaluations involve a computationally expensive simulation model their cost becomes prohibitive. In this paper we analyze the use of surrogate models in order to enhance the performance of a clonal selection algorithm. Computational experiments are conducted to assess the performance of the presented techniques using a benchmark with 22 test-problems under a fixed budget of objective function evaluations. The comparisons show that for most cases the use of surrogate models improve significantly the performance of the baseline clonal selection algorithm.

A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation

Constrained optimization problems are common in the sciences, engineering, and economics. Due to the growing complexity of the problems tackled, nature-inspired metaheuristics in general, and evolutionary algorithms in particular, are becoming increasingly popular. As move operators (recombination and mutation) are usually blind to the constraints, most metaheuristics must be equipped with a constraint handling technique. Although conceptually simple, penalty techniques usually require user-defined problem-dependent parameters, which often significantly impact the performance of a metaheuristic. A penalty technique is said to be adaptive when it automatically sets the values of all parameters involved using feedback from the search process without user intervention. This chapter presents a survey of the most relevant adaptive penalty techniques from the literature, identifies the main concepts used in the adaptation process, as well as observed shortcomings, and suggests further work in order to increase the understanding of such techniques.

Variants of an adaptive penalty scheme for steady-state genetic algorithms in engineering optimization

Purpose – The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems. Design/methodology/approach – For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coe...

Comparing two constraint handling techniques in a binary-coded genetic algorithm for optimization problems

In this paper the relative performance of two constraint handling techniques, namely a parameter-less adaptive penalty method (APM) and the stochastic ranking method (SR), is studied in the context of continuous parameter constrained optimization problems. Both techniques are used within the same search engine, a binary-coded genetic algorithm.

A Faster Clonal Selection Algorithm for Expensive Optimization Problems

Artificial Immune Systems (AISs) are computational methods, inspired by the biological immune system, that can be applied to solve optimization problems. In this paper we propose the use of a similarity-based surrogate model in conjunction with a clonal selection algorithm in order to improve its performance when solving optimization problems involving computationally expensive objective functions. Computational experiments to assess the performance of the proposed procedure using 23 test-problems from the literature are presented.