The Social Engineering Optimizer (SEO)

被引:218
作者
Fathollahi-Fard, Amir Mohammad [1 ]
Hajiaghaei-Keshteli, Mostafa [1 ]
Tavakkoli-Moghaddam, Reza [2 ,3 ]
机构
[1] Univ Sci & Technol Mazandaran, Dept Ind Engn, Behshahr, Iran
[2] Univ Tehran, Coll Engn, Sch Ind Engn, Tehran, Iran
[3] Arts & Metiers Paris Tech, LCFC, Metz, France
关键词
Social Engineering Optimizer (SEO); Meta-heuristics; Single-solution; Optimization techniques; Engineering applications; CHARGE TRANSPORTATION PROBLEM; MACHINE SCHEDULING PROBLEM; NATURE-INSPIRED ALGORITHM; LOOP SUPPLY CHAIN; FIXED-CHARGE; GENETIC ALGORITHM; SEARCH ALGORITHM; SWARM ALGORITHM; METAHEURISTICS; EARLINESS;
D O I
10.1016/j.engappai.2018.04.009
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Although several meta-heuristics have been developed in the last two decades, most of them are population based, undergo many steps along with several parameters that make them hard to understand and code. In addition, there are same procedures in recent metaheuristics which make them similar. So, the researchers usually are confused to select a metaheuristic and cannot find any superiority or at least in any algorithms. Because of this, the researchers still use the old algorithms instead of the recent ones. Contrary to previous work, this paper aims to develop a simple, intelligent and new single-solution algorithm that has just four main steps and three simple parameters to tune. Social Engineering Optimizer (SEO) starts with two initial solutions divided into attacker and defender. The attacker obtains the rules of Social Engineering techniques to reach its desired goals. By these simple features, the algorithm does precisely both intensification and diversification phases. The basis of the algorithm depends on how an attacker attacks to a defender by four different associated techniques. Finally, the proposed SEO is applied to solve a set of benchmark functions, important engineering and multi-objective optimization problems. The result shows its superiority in comparison with other well-known and recent meta-heuristics.
引用
收藏
页码:267 / 293
页数:27
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