OPTIMIZATION OF CONCRETE I-BEAMS USING A NEW HYBRID GLOWWORM SWARM ALGORITHM

TATIANA GARCíA-SEGURA, VICTOR YEPES, JOSé VICENTE MARTí, JULIáN ALCALá

Abstract


A NEW HYBRID GLOWWORM SWARM ALGORITHM (SAGSO) SHOWS AN EFFECTIVE PERFORMANCE FOR SOLVING STRUCTURE OPTIMIZATION PROBLEMS. THE STRUCTURE PROPOSED IS A SIMPLY-SUPPORTED CONCRETE I-BEAM DEFINED BY 20 VARIABLES. EIGHT DIFFERENT CONCRETE MIXTURES ARE STUDIED, VARYING THE COMPRESSIVE STRENGTH GRADE AND COMPACTING SYSTEM. THE SOLUTIONS ARE EVALUATED FOLLOWING THE SPANISH CODE FOR STRUCTURAL CONCRETE. THE ALGORITHM IS APPLIED TO TWO OBJECTIVE FUNCTIONS, NAMELY THE EMBEDDED CO2 EMISSIONS AND THE ECONOMIC COST OF THE STRUCTURE. THE ABILITY OF GLOWWORM SWARM OPTIMIZATION (GSO) TO SEARCH IN THE ENTIRE SOLUTION SPACE IS COMBINED WITH THE LOCAL SEARCH BY SIMULATED ANNEALING (SA) TO OBTAIN ENCOURAGING RESULTS. FINALLY, THE HYBRID ALGORITHM CAN SOLVE STRUCTURAL OPTIMIZATION PROBLEMS APPLIED TO DISCRETE VARIABLES.

Keywords


HYBRID GLOWWORM SWARM ALGORITHM; CONCRETE I-BEAM; CO2 EMISSIONS; STRUCTURAL OPTIMIZATION; SELF-COMPACTING

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