MULTI-OBJECTIVE OPTIMIZATION WITH KRIGING SURROGATES USING “MOKO”, AN OPEN SOURCE PACKAGE
MANY MODERN REAL-WORLD DESIGNS RELY ON THE OPTIMIZATION OF MUL-TIPLE COMPETING GOALS. FOR EXAMPLE, MOST COMPONENTS DESIGNED FOR THE AEROSPACE INDUSTRY MUST MEET SOME CONFLICTING EXPECTATIONS. IN SUCH APPLICATIONS, LOW WEIGHT, LOW COST, HIGH RELIABILITY, AND EASY MANUFACTURABILITY ARE DESIRABLE. IN SOME CASES, BOUNDS FOR THESE REQUIREMENTS ARE NOT CLEAR, AND PERFORMING MONO-OBJECTIVE OPTIMIZATIONS MIGHT NOT PROVIDE A GOOD LANDSCAPE OF OPTIMAL CHOICES. FOR THESE CASES, FINDING A SET OF PARETO OPTIMAL DESIGNS MIGHT GIVE THE DESIGNER A COMPREHENSIVE SET OF OPTIONS FROM WHICH TO CHOOSE THE BEST DESIGN. THIS ARTICLE SHOWS THE MAIN FEATURES OF AN OPEN SOURCE PACKAGE, DEVELOPED BY THE PRESENT AUTHORS, TO SOLVE CONSTRAINED MULTI-OBJECTIVE PROBLEMS. THE PACKAGE, NAMED MOKO (ACRONYM FOR MULTI-OBJECTIVE KRIGING OPTIMIZATION), WAS BUILT UNDER THE OPEN SOURCE PROGRAMMING LANGUAGE R. POPULAR KRIGING BASED MULTI-OBJECTIVE OPTIMIZATION STRATEGIES, AS THE EX-PECTED VOLUME IMPROVEMENT AND THE WEIGHTED EXPECTED IMPROVE-MENT, ARE AVAILABLE IN THE PACKAGE. IN ADDITION, AN APPROACH PRO-POSED BY THE AUTHORS, BASED ON THE EXPLORATION USING A PREDICTED PARETO FRONT IS IMPLEMENTED. THE LATTER APPROACH SHOWED TO BE MORE EFFICIENT THAN THE TWO OTHER TECHNIQUES IN SOME CASE STUDIES PERFORMED BY THE AUTHORS WITH MOKO.
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