A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization Problems

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

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Performance Analysis of Some New Hybrid Metaheuristic Algorithms for High‐Dimensional Optimization Problems

Electrical and Electronic Devices, Circuits, and Materials ◽

10.1002/9781119755104.ch14 ◽

2021 ◽

pp. 251-284

Author(s):

Souvik Ganguli ◽

Gagandeep Kaur ◽

Prasanta Sarkar

Keyword(s):

Performance Analysis ◽

Optimization Problems ◽

Metaheuristic Algorithms ◽

High Dimensional ◽

Hybrid Metaheuristic ◽

Dimensional Optimization

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Solving the multi-level location routing problem considering the environmental impact using a hybrid metaheuristic

International Journal of Engineering Business Management ◽

10.1177/18479790211017353 ◽

2021 ◽

Vol 13 ◽

pp. 184797902110173

Author(s):

Chalermchat Theeraviriya ◽

Kitchanut Ruamboon ◽

Nat Praseeratasang

Keyword(s):

Environmental Impact ◽

Optimization Problems ◽

Reverse Flow ◽

Neighborhood Search ◽

Location Routing Problem ◽

Routing Problem ◽

Hybrid Metaheuristic ◽

Combinatorial Optimization Problems ◽

Location Routing ◽

Multi Level

The multi-level location routing problem (MLLRP) is an extension of the capacitated location routing problem (CLRP). MLLRPs are considered a class of combinatorial optimization problems that arise in transportation applications, such as agricultural logistic planning. Along with concerns regarding the environmental harmfulness, recent studies have considered “green” logistics. This paper addressed a low environmental impact model for MLLRP by considering characteristics, emissions, and traffic congestion that have not appeared in the recent literature. The mathematical model was formulated to deal with a reverse flow problem, which was a real case that occurs in Thailand agriculture. We developed a hybrid metaheuristic algorithm to solve the MLLRP by integrating a variable neighborhood search (VNS) with an adaptive large neighborhood search (ALNS). The experimental results shown that the hybrid algorithm had clear advantages in the time consumption and quality of the solution. The extended study indicated that the proposed algorithm obtained competitive results compared with the previously published methods. The proposed practice is useful not only for the agricultural industry but also for other industries.

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HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622021500176 ◽

2021 ◽

Vol 20 (02) ◽

pp. 775-808

Author(s):

Morteza Karimzadeh Parizi ◽

Farshid Keynia ◽

Amid Khatibi Bardsiri

Keyword(s):

Local Search ◽

Optimization Problems ◽

High Capacity ◽

Metaheuristic Algorithms ◽

Development Effort ◽

Effort Estimation ◽

Lévy Flight ◽

Levy Flight ◽

Hybrid Metaheuristic ◽

Real World Datasets

Hybrid metaheuristic algorithms have recently become an interesting topic in solving optimization problems. The woodpecker mating algorithm (WMA) and the sine cosine algorithm (SCA) have been integrated in this paper to propose a hybrid metaheuristic algorithm for solving optimization problems called HSCWMA. Despite the high capacity of the WMA algorithm for exploration, this algorithm needs to augment exploitation especially in initial iterations. Also, the sine and cosine relations used in the SCA provide the good exploitation for this algorithm, but SCA suffers the lack of an efficient process for the implementation of effective exploration. In HSCWMA, the modified mathematical search functions of SCA by Levy flight mechanism is applied to update the female woodpeckers in WMA. Moreover, the local search memory is used for all search elements in the proposed hybrid algorithm. The goal of proposing the HSCWMA is to use exploration capability of WMA and Levy flight, utilize exploitation susceptibility of the SCA and the local search memory, for developing exploration and exploitation qualification, and providing the dynamic balance between these two phases. For efficiency evaluation, the proposed algorithm is tested on 28 mathematical benchmark functions. The HSCWMA algorithm has been compared with a series of the most recent and popular metaheuristic algorithms and it outperforms them for solving nonconvex, inseparable, and highly complex optimization problems. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the software development effort estimation (SDEE) problem on three real-world datasets. The simulation results proved the superior and promising performance of the HSCWMA algorithm in the majority of evaluations.

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