scholarly journals Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness

2020 ◽  
Vol 22 (3) ◽  
pp. 128-139
Author(s):  
Esra'a Alkafaween ◽  
Ahmad B. A. Hassanat
Keyword(s):  
Success Factors ◽  
Optimization Problems ◽  
Combinatorial Problems ◽  
Mutation Operator ◽  
Efficient Tool ◽  
Key Success Factors ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Symmetric Traveling Salesman Problem ◽  
Hybrid Mutation Operator

Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution. The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA. Various mutation operators exist to solve hard combinatorial problems such as the TSP. In this paper, we propose a hybrid mutation operator called "IRGIBNNM", this mutation is a combination of two existing mutations; a knowledgebased mutation, and a random-based mutation. We also improve the existing “select best mutation” strategy using the proposed mutation. We conducted several experiments on twelve benchmark Symmetric traveling salesman problem (STSP) instances. The results of our experiments show the efficiency of the proposed mutation, particularly when we use it with some other mutations.

scholarly journals Subspace Clustering Mutation Operator for Developing Convergent Differential Evolution Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Zhongbo Hu ◽  
Shengwu Xiong ◽  
Xiuhua Wang ◽  
Qinghua Su ◽  
Mianfang Liu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Subspace Clustering ◽  
Convergence In Probability ◽  
Mutation Operator ◽  
Mutation Operators ◽  
Evolution Algorithm ◽  
The Difference ◽  
Theoretical Analyses

Many researches have identified that differential evolution algorithm (DE) is one of the most powerful stochastic real-parameter algorithms for global optimization problems. However, a stagnation problem still exists in DE variants. In order to overcome the disadvantage, two improvement ideas have gradually appeared recently. One is to combine multiple mutation operators for balancing the exploration and exploitation ability. The other is to develop convergent DE variants in theory for decreasing the occurrence probability of the stagnation. Given that, this paper proposes a subspace clustering mutation operator, called SC_qrtop. Five DE variants, which hold global convergence in probability, are then developed by combining the proposed operator and five mutation operators of DE, respectively. The SC_qrtop randomly selects an elite individual as a perturbation’s center and employs the difference between two randomly generated boundary individuals as a perturbation’s step. Theoretical analyses and numerical simulations demonstrate that SC_qrtop prefers to search in the orthogonal subspace centering on the elite individual. Experimental results on CEC2005 benchmark functions indicate that all five convergent DE variants with SC_qrtop mutation outperform the corresponding DE algorithms.

scholarly journals A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250951
Author(s):  
Xuxu Zhong ◽  
Meijun Duan ◽  
Xiao Zhang ◽  
Peng Cheng
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Mutation Operator ◽  
Exploration And Exploitation ◽  
Two Phase ◽  
Crossover Probability ◽  
Insurance Mechanism ◽  
Hybrid Mutation Operator ◽  
Selection Of ◽  
Classical Mutation

Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

Key Success Factors of Organizational Success: A Study of MULA Car International

2020 ◽  
Vol 3 (2) ◽  
pp. 46-56
Author(s):  
Muhammad Ashraff ◽  
Daisy Mui Hung Kee ◽  
Roshini A/P Subramaniam ◽  
Nur Hazimah ◽  
Nur Aina Syafiqah
Keyword(s):  
Success Factors ◽  
Organizational Success ◽  
Key Success Factors

MANAGING DIGITAL TRANSFORMATION IN DIGITAL BUSINESS ECOSYSTEMS

2021 ◽  
Author(s):  
TIINA TAWASTSTJERNA ◽  
HEIDI OLANDER
Keyword(s):  
Success Factors ◽  
Collaborative Governance ◽  
Digital Transformation ◽  
Key Success Factors ◽  
Multiple Partners ◽  
Business Ecosystems

Previous research has increased our understanding of digital transformation (DT) and digital business ecosystems as independent topics. Less is known about how DT unfolds in digital business ecosystems. Such collaborative creation of digital innovations is affected by individual actors and by ecosystem as a whole. Based on an empirical case study of an ecosystem facilitator company and its digital business ecosystems as embedded cases, this paper contributes to the understanding of key success factors in new digital business ecosystems. The findings support collaborative governance as an important tool in leading the DT among multiple partners. Moreover, the findings present the concept of a common rulebook, including the practices, principles, guidelines, tools, handshakes, and boundaries, as an enabler for ways of working in an ecosystem. Managers can use this paper to increase their understanding on the governance of digital business ecosystems and to clarify their organisational expectations when participating in joint endeavours involving DT.

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