Multiobjective optimization of area‐to‐point heat conduction structure using binary quantum‐behaved PSO and Tchebycheff decomposition method

Abstract

A multiobjective optimization of area‐to‐point heat conduction to minimize both mean temperature and temperature variance is conducted based on a decomposition‐based multiobjective binary quantum‐behaved particle swarm optimization (PSO) method (MOMBQPSO/D). The MOMBQPSO/D adopts the framework of the multiobjective evolutionary algorithm based on decomposition and modifies the binary quantum‐behaved PSO. In the first step of the MOMBQPSO/D, the multiobjective area‐to‐point problem is divided into a series of subproblems using Tchebycheff decomposition method. Next, all the subproblems are solved simultaneously using the modified binary quantum‐behaved PSO. Finally, a series of Pareto optimal solutions representing the conducting path structures are stepwise selected from the solutions to the subproblems. The features of the Pareto optimality‐based conducting paths and cooling performance are described. In addition, the effects of the conductive material quantity, optimization objective, heat sink location, and heat source distribution on the conducting path structure and cooling performance are discussed.

Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study

“…Cai et al [35] proposed hybridizing a multi-objective evolutionary algorithm with a quantum-behaved PSO after dividing the problem into subproblems with Tchebycheff's decomposition: Decomposition-based Multi-Objective Binary Quantum-behaved Particle Swarm Optimization (MOMBQPSO/D). The algorithm minimizes the temperature mean and deviation in area-to-point heat conduction.…”