Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

Abstract

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

Multiobjective Automatic Parameter Calibration of a Hydrological Model

“…An example of the variable balancing approach is the so-called parameter-setting-free (PSF) method, which has been used by a few researchers in the field of applied mathematics for removing the burden on the user in selecting algorithm parameters [41][42][43].…”

“…The former is defined as the algorithm's ability to search wide areas of the solution space (e.g., the random search of HS), and the latter refines the neighborhood of previously found promising areas in solution space (e.g., the crossover of GA). However, some studies reported that the balance should be variable to guarantee the best performance [41][42][43] because the effectiveness of wide search and fine-tuning depends on the optimization phase. Cuevas et al [44] stated that a common strategy for balancing exploration and exploitation is to start with exploration and then gradually increase exploitation as good fitness points are identified.…”

A Quasi-Oppositional Method for Output Tracking Control by Swarm-Based MPID Controller on AC/HVDC Interconnected Systems With Virtual Inertia Emulation

“…In this SBOA, like some other algorithms, the most determinative factor for constructing the effective exploitation and exploration process is the operation of the honey bees foraging. The obtained different experiences demonstrate to be well-satisfied with the ABC algorithm for the solution of the multidimensional optimization problems (such as [62] - [63]). The bee population is classified into three main groups which work in the colony: (i) employed, (ii) onlooker and (iii) scout bees.…”

A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications

“…e performance of MBHSA outperforms the existing variants in terms of convergence and diversity. Sabrinath et al [50] extended PAHSA to solve multiobjective optimization problems. e weighted sum Mathematical Problems in Engineering approach was used to assign the weightages to performance indices.…”