An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Sun, Weiyuan; Wang, Jiesheng; Wei, Xian

doi:10.3390/sym10060210

Cited by 46 publications

(25 citation statements)

References 33 publications

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“…From the computational result, we can conclude that metaheuristics like DE can successfully solve the proposed problem. The other metaheuristics, which has an excellent search mechanism such as the polar bear optimization [36], the Dragonfly algorithm [37], and the whale optimization algorithm [38], should successfully solve the proposed problem. The proposed algorithm can be more interesting if it can find the maximum profit for the farmers and keep the maximum income for the drivers so that they are willing to follow the constructed plan.…”

Section: Discussionmentioning

confidence: 99%

Solving a Special Case of the Generalized Assignment Problem Using the Modified Differential Evolution Algorithms: A Case Study in Sugarcane Harvesting

Srivarapongse

Pijitbanjong

2019

Journal of Open Innovation: Technology, Market, and Complexity

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We proposed and created a methodology to solve a real-world problem, which is a special case of the generalized assignment problem. The problem consists of assigning drivers to harvesters, which will then be assigned to harvest sugarcane in order to maximize daily profit. A set of drivers have various levels of experience. Therefore, a different capability to harvest sugarcane leads to a range of daily wages. Each harvester has different operating years and engine size, which affects its fuel consumption rate and capacity to harvest sugarcane, respectively. Assigning a worker to a harvester can improve the fuel consumption and efficiency of the harvester. We developed a mathematical model to reflect this problem and to solve it to find the maximum outcome using Lingo v.11 commercial optimization software. Since Lingo v.11 is limited to solving only small-size test instances, for medium to large test instances, four modified differential evolution (MDE) algorithms were used to solve the problem: MDE-1, MDE-2, MDE-3, and MDE-4. MDE-2 was found to be the best proposed heuristics because it has intensification and diversification ability. MDE has been tested with the case study. We tried to increase the daily profit by implementing three strategies: (1) change all harvesters that are more than five years old, (2) train drivers to reach maximum capacity, and (3) a combination of 1 and 2. Each strategy has a different investment. The breakeven point (number of days) to return the investment was calculated from the increase of daily profit. The computational results show that strategy 2 is the best because it has the quickest rate of investment return rate. However, this strategy has a disadvantage, since it is possible that drivers may leave the company if they have been highly trained. Moreover, strategy 1 has an acceptable break-even point at 392 days.

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Section: Discussionmentioning

confidence: 99%

Solving a Special Case of the Generalized Assignment Problem Using the Modified Differential Evolution Algorithms: A Case Study in Sugarcane Harvesting

Srivarapongse

Pijitbanjong

2019

Journal of Open Innovation: Technology, Market, and Complexity

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“…r is a random number in the range of [0,1]. f is the fragrance coefficient which means the perceived magnitude of the fragrance, all butterflies can send out some fragrance that makes butterflies attract each other, and f can be updated in (3).…”

Section: The Proposed Heuristic Optimization Algorithmmentioning

confidence: 99%

“…Step 2: Calculate the fragrance coefficient of each butterfly using (3). Record the best butterfly position whose concentration of the fragrance is maximum.…”

Section: The Proposed Heuristic Optimization Algorithmmentioning

confidence: 99%

A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems

et al. 2020

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For shortcomings of poor exploaration and parameter complexities of the butterfly optimization algorithm, an improved butterfly optimization algorithm based the self-adaption method (SABOA) was proposed to extremely enhance the searching accuracy and the iteration capability. SABOA has advantages of having fewer parameters, the simple algorithm structure, and the strong precision. First, a new fragrance coefficient was added to the basic butterfly optimization algorithm. Then, new iteration and updating strategies were introduced in global searching and local searching phases. Finally, this paper tested different optimization problems including low-high functions and constrained problems, and the obtained results were compared with other well-known algorithms, this paper also drew various mathematical statistics figures to comprehensively analyze searching performances of the proposed algorithms. The experimental results show that SABOA can get less number of function evaluations compared to other considered algorithms, which illustrates that SABOA has great searching balance, large exploration, and high iterative speed. INDEX TERMS Butterfly optimization algorithm, global optimization, constrained problem. I. INTRODUCTION In applied mathematics and engineering fields, there are numerous optimization problems whose calculated solutions are in a large and complex searching space. Traditional optimization methods, including the steepest descent method, the conjugate gradient method, the variable scale method, and Newton method, can only deal with objective functions that are simple, continuously differentiable, and high order differentiable [1]-[3]. With the increasing of problem diversities and problem complexities, traditional optimization methods can not meet different requirements of higher calculation speed and lower average percentage error, so it is crucial to find for new optimization methods that have fast calculation speed and perfect convergence abilities [4], [5]. With the development of artificial intelligence, digitization, and computer technologies, numerous meta-heuristic optimization algorithms have been increasingly proposed and applied in science and engineering fields [6]-[8]. Meta-heuristic algorithms own characteristics of selforganizing, mutual compatibility, simplicity, parallelism, wholeness and harmony. Meta-heuristic algorithms work The associate editor coordinating the review of this manuscript and approving it for publication was Jagdish Chand Bansal.

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“…The improved WOA (IWOA), that adopts a differential evolution (DE) mutation operator and utilizes adaptive strategy for balancing between exploitation and exploration was proposed in [25] and proved to be more efficient an approach than the original WOA. Another improved WOA version that is based on different searching paths and perceptual disturbance was proposed and tested on 23 standard unconstrained benchmarks and obtained better result quality than the original version [84].…”

Section: Deficiences Of the Original Woa Approach And Related Literaturementioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Cited by 46 publications

References 33 publications

Solving a Special Case of the Generalized Assignment Problem Using the Modified Differential Evolution Algorithms: A Case Study in Sugarcane Harvesting

Solving a Special Case of the Generalized Assignment Problem Using the Modified Differential Evolution Algorithms: A Case Study in Sugarcane Harvesting

A Self-Adaption Butterfly Optimization Algorithm for Numerical Optimization Problems

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

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