A novel Whale Optimization Algorithm integrated with Nelder–Mead simplex for multi-objective optimization problems

In manufacturing industries, selecting the appropriate cutting parameters is essential to improve the product quality. As a result, the applications of optimization techniques in metal cutting processes is vital for a quality product. Due to the complex nature of the machining processes, single objective optimization approaches have limitations, since several different and contradictory objectives must be simultaneously optimized. Multi-objective optimization method is introduced to find the optimum cutting parameters to avoid this dilemma. The main objective of this paper is to develop a multi-objective optimization algorithm using the hybrid Whale Optimization Algorithm (WOA). In order to perform the multi-objective optimization, grey analysis is integrated with the WOA algorithm. In this paper, Stainless Steel 304 is utilized for turning operation to study the effect of machining parameters such as cutting speed, feed rate and depth of cut on surface roughness, cutting forces, power, peak tool temperature, material removal rate and heat rate. The output parameters are obtained through series of simulations and experiments. Then by using this hybrid optimization algorithm the optimum machining conditions for turning operation is achieved by considering unit cost and quality of production. It is also found that with the change of output parameter weightage, the optimum cutting condition varies. In addition to that, the effects of different cutting parameters on surface roughness and power consumption are analysed.

Download Full-text

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

Electronics ◽

10.3390/electronics10232975 ◽

2021 ◽

Vol 10 (23) ◽

pp. 2975

Author(s):

Mohammad H. Nadimi-Shahraki ◽

Shokooh Taghian ◽

Seyedali Mirjalili ◽

Laith Abualigah ◽

Mohamed Abd Abd Elaziz ◽

...

Keyword(s):

Power System ◽

Optimization Algorithm ◽

Power Flow ◽

Optimal Power Flow ◽

Optimization Problems ◽

Whale Optimization Algorithm ◽

Objective Functions ◽

Multi Objective ◽

Optimal Power ◽

Whale Optimization

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

Download Full-text

Opposition-Based Multi-Objective Whale Optimization Algorithm with Multi-Leader Guiding

10.21203/rs.3.rs-335132/v1 ◽

2021 ◽

Author(s):

Yang Li ◽

Wei-gang Li ◽

Yun-tao Zhao ◽

Ao Liu

Keyword(s):

Optimization Algorithm ◽

Pareto Front ◽

Heuristic Algorithms ◽

Optimization Problems ◽

Whale Optimization Algorithm ◽

Benchmark Problems ◽

Initial Population ◽

Multi Objective ◽

Opposition Based Learning ◽

Whale Optimization

Abstract Over the years, heuristic algorithms have been widely studied, especially in multi-objective optimization problems (MOPs). The multi-objective whale optimization algorithm based on multi-leader guiding (MOWOAMLG) is proposed in this paper, which is the multi-objective version of whale optimization algorithm (WOA). The proposed algorithm adopts several improvements to enhance optimization performance. First, multiple leadership solutions guide the population to search the sparse space to achieve more homogeneous exploration in per iteration, and the leadership solutions are selected on the Pareto front by grid mechanism and the principle of maximum crowding distance. Second, the differential evolution (DE) is employed to generate the offspring for the leadership solutions, while WOA is employed for the ordinary solutions. In addition, a novel opposition-based learning (OBL) strategy is developed to improve the distribution of the initial population. To show the significance of the proposed algorithm, it is tested on the 20 bi-objective and tri-objective unconstrained benchmark problems of varying nature and complexities. The result of numerical experiments shows that the proposed algorithm has competitive advantages in convergence and distribution while compared with other 10 classic or state-of-the-arts algorithms. The convergence curve of IGD indicates that MOWOAMLG is able to obtain good Pareto front in cost of fewer optimization iterations. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned results have indicated that MOWOAMLG is comparatively effective and efficient to solve MOPs.

Download Full-text

Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽

10.3390/s21082628 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2628

Author(s):

Mengxing Huang ◽

Qianhao Zhai ◽

Yinjie Chen ◽

Siling Feng ◽

Feng Shu

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Solution Set ◽

Whale Optimization Algorithm ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Multi Objective ◽

Whale Optimization ◽

Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

Download Full-text

An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽

10.3390/sym13010048 ◽

2020 ◽

Vol 13 (1) ◽

pp. 48

Author(s):

Jin Zhang ◽

Li Hong ◽

Qing Liu

Keyword(s):

Traveling Salesman Problem ◽

Optimization Algorithm ◽

Optimization Problems ◽

Relevant Literature ◽

Population Diversity ◽

Traveling Salesman ◽

Whale Optimization Algorithm ◽

Neighborhood Search ◽

Whale Optimization ◽

The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

Download Full-text

A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽

10.3390/math9131477 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1477

Author(s):

Chun-Yao Lee ◽

Guang-Lin Zhuo

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Optimization Problems ◽

Whale Optimization Algorithm ◽

Initial Population ◽

Test Functions ◽

Benchmark Test ◽

Thermal Exchange ◽

Whale Optimization ◽

Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

Download Full-text