Multi-objective Topology Optimization with Ant Colony Optimization and Genetic Algorithms

Swarm intelligence optimization techniques are widely used in topology optimization of compliant mechanisms. The Ant Colony Optimization has been implemented in various forms to account for material density distribution inside a design domain. In this paper, the Ant Colony Optimization technique is applied in a unique manner to make it feasible to optimize for the beam elements’ cross-section and material density simultaneously. The optimum material distribution algorithm is governed by two various techniques. The first technique treats the material density as an independent design variable while the second technique correlates the material density with the pheromone intensity level. Both algorithms are tested for a micro displacement amplifier and the resulting optimized topologies are benchmarked against reported literature. The proposed techniques culminated in high performance and effective designs that surpass those presented in previous work.

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Modified A* Algorithm integrated with ant colony optimization for multi-objective route-finding; case study: Yazd

Applied Soft Computing ◽

10.1016/j.asoc.2021.107877 ◽

2021 ◽

Vol 113 ◽

pp. 107877

Author(s):

Leila Pasandi ◽

Mehrnaz Hooshmand ◽

Morteza Rahbar

Keyword(s):

Ant Colony Optimization ◽

Ant Colony ◽

A Algorithm ◽

Multi Objective ◽

Route Finding

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A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2016100108 ◽

2016 ◽

Vol 5 (4) ◽

pp. 165-191 ◽

Cited By ~ 1

Author(s):

Boominathan Perumal ◽

Aramudhan M.

Keyword(s):

Ant Colony Optimization ◽

Virtual Machine ◽

Bin Packing ◽

Virtual Machines ◽

Transition Probability ◽

Virtual Machine Placement ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Placement Problem ◽

Multi Objective

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

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