Abstract
This article introduces a metaheuristic algorithm to solve engineering design optimization problems. The algorithm is based on the concept of diversity and independence that is aggregated in the average design of a population of designs containing information dispersed through a variety of points, and on the concept of intensification represented by the best design. The algorithm is population-based, where the population individual designs are randomly generated. The population can be normally or uniformly generated. The algorithm may start either with points randomly generated or with a designer preferred trial guess. The algorithm is validated using standard classical unconstrained and constrained engineering optimum design test problems reported in the literature. The results presented indicate that the proposed algorithm is a very simple alternative to solve this kind of problems. They compare well with the analytical solutions and/or the best results achieved so far. Two constrained problem analytical solutions not found in the literature are presented in annex.
Highlights
With the advent of fast, cheap and reliable computing power over the last decades, in addition to the application of classic optimization to larger and larger size problems, new alternative algorithms operating in a different fashion have been developed
The purpose of heuristic algorithms applied to optimization problems is to search a solution to them by trial-and-error in a satisfying amount of computing time
Metaheuristics refers to higher-level algorithms combining lower-level techniques and tactics for exploration and exploitation of the design space
Summary
With the advent of fast, cheap and reliable computing power over the last decades, in addition to the application of classic optimization to larger and larger size problems, new alternative algorithms operating in a different fashion have been developed. One of the roles of injected randomness in stochastic search is to allow for movements to unexplored areas of the search space that may contain an unexpectedly good design This is especially relevant when the search is stalled near a local solution. The necessary conditions for a crowd to be wise include diversity, independence, and a specific type of decentralization These conditions are essential to making good decisions which are the result of disagreement and contest rather than consensus or compromise. The algorithm is population-based, where the population individual designs are randomly generated. These individuals are diversified and independent since their design variables values are chosen stochastically without any correlation. The present algorithm is applied to several constrained and unconstrained test functions as well as to typical engineering design problems
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