Bin packing problem (BPP) is a combinatorial optimization problem with a wide range of applications in fields such as financial budgeting, load balancing, project management, supply chain management. Harmony search algorithm (HSA) is widely used for various real-world and engineering problems due to its simplicity and efficient problem solving capability. Literature shows that basic HSA needs improvement in search capability as the performance of the algorithm degrades with increase in the problem complexity. This paper presents HSA with improved exploration and exploitation capability coupled with local iterative search based on random swap operator for solving BPP. The study uses the despotism based approach presented by Yadav et al. (2012) [Yadav P., Kumar R., Panda S.K., Chang, C. S. (2012). An intelligent tuned harmony search algorithm for optimisation. Information Sciences, 196, 47-72.] to divide Harmony memory (HM) into two categories which helps to maintain balance between exploration and exploitation. Secondly, local iterative search explores multiple neighborhoods by exponentially swapping components of solution vectors. A problem specific HM representation, HM re-initialization strategy and two adaptive PAR strategies are tested. The performance of proposed HSA is evaluated on 180 benchmark instances which consists of 100, 200 and 500 objects. Evaluation metrics such as best, mean, success rate, acceleration rate and improvement measures are used to compare HSA variations. The performance of the HSA with iterative local search outperforms other two variations of HSA.

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

Bin packing problem is a constrained optimization problem with a huge search space due to large combinations. Bin packing problem has a wide range of applications in multiple fields. This paper presents harmony search algorithm with different initialization and adaptive PAR strategies for solving bin packing problem. The proposed Harmony search (HS) variations tests two partial feasible initialization strategies for bin packing problem. The paper presents adaptive PAR strategies for better exploration and exploitation of HS algorithm. The PAR values are tuned in every iteration. Improved initialization strategy, population initialization after premature convergence and adaptive PAR leads to the better exploration of harmony search algorithm for bin packing problem. The performance of variations are tested over 120 benchmark instances with 100 and 200 objects with varying complexities. The results show that improved HS performs better than basic HS with respect to best, mean, convergence rate. The performance of algorithms is tested with varying harmony memory size and harmony memory considering rate. Results show that variation in these two parameter values has less effect on performance of improved versions.