# Elite Opposition-Based Social Spider Optimization Algorithm for Global Function Optimization
## 2 The Social Spider Optimization Algorithm
### Computational Procedure
Figure 10: Dim = 4, evolution curves of fitness value for \(f_{23}\). Figure 14: Dim = 30, variance diagram of fitness value for \(f_{9}\). Figure 13: Dim = 30, variance diagram of fitness value for \(f_{7}\). Figure 15: Dim = 30, variance diagram of fitness value for \(f_{10}\). Figure 16: Dim = 30, variance diagram of fitness value for \(f_{11}\). Figure 17: Dim = 2, variance diagram of fitness value for \(f_{16}\). Figure 18: Dim = 2, variance diagram of fitness value for \(f_{18}\). In addition, for EOSSO can get better best fitness value and mean fitness value and worst fitness value, but standard deviation are worse than that of SSO. After analyzing Tables 4-6, a conclusion can be easily drawn that EOSSO has a great ability for solving function optimization problems according to the experimental results. Figures 1-10 show the evolution curves of fitness value for \(f_{1},f_{4},f_{7},f_{9},f_{10},f_{11},f_{16},f_{18},f_{19}\) and \(f_{23}\). From these Figures, we can easily find that EOSSO converge faster than other population based algorithms mentioned above, and the values obtained by EOSSO are closer to the optimal value of benchmark functions. These show that EOSSO has a faster convergence speed and a better precision than SSO and other population based algorithms. Figures 11-20 show the anova test of global minimum for \(f_{1},f_{4},f_{7},f_{9},f_{10},f_{11},f_{16},f_{18},f_{19}\) and \(f_{23}\). From these figures, we can discover that the standard deviation of EOSSO is much smaller, and the number of outlier is less than other algorithms. These imply that EOSSO has a great performance with a high degree of stability. In sum, proposed EOSSO is an algorithm with fast convergence speed, high level of precision and a great performance of stability. In Section 4.2, 23 standard benchmark functions are selected to evaluate the performance of elite opposition-based Social Spider Optimization algorithm (EOSSO).\(f_{1}\)-\(f_{7}\) are unimodal function, \(f_{8}\)-\(f_{13}\) are multimodal function, \(f_{14}\)-\(f_{23}\) are fixed-dimension multimodal benchmark function. Experiment results are listed in Tables 4-6. Figures 1-20 are evaluation curves of fitness values and anova test of global minimum for \(f_{1},f_{4},f_{7},f_{9},f_{10},f_{11},f_{16},f_{18},f_{19}\) and \(f_{23}\). The results in tables show that a more precise solution can be found by EOSSO. Figures listed in paper reflect a fact that EOSSO has a faster convergence speed and a higher stability. ## 6 Conclusions and Future Works

In order to overcome the disadvantage of Social Spider Optimization algorithm that it is still easy to fall into a local optimal solution, this paper presents a novel SSO algorithm called EOSSO by using OBL and the elite selection mechanism. Opposition-based Learning (OBL) is a new concept in computational intelligence and it has been proven to be an effective strategy to improve performance of various optimization algorithms. The main idea behind OBL is to transform solutions in the current search space to a new search space. By simultaneously considering the solutions in the current search space and the transformed search space, OBL can provide a higher chance of finding solutions that are closer to the global optimum. This kind of mechanism enhances the diversity of the population, which helps to improve its exploration ability. From the results of the 23 benchmark functions, the performance of EOSSO is better than, or at least comparable with other population-based algorithm mentioned in this paper. EOSSO has a fast convergence speed, a relatively high degree of stability and it is also much more accurate in precision. For EOSSO, there are various issues that still deserve further study. Firstly, multi-objective optimization problems can be seen here and there in the real world. Compared with single objective optimization problems, it is often very challenging to obtain high-equality solution.