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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 936374, 21 pages
http://dx.doi.org/10.1155/2014/936374
Research Article

A Free Search Krill Herd Algorithm for Functions Optimization

College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China

Received 5 April 2014; Revised 13 May 2014; Accepted 21 May 2014; Published 19 June 2014

Academic Editor: Yang Xu

Copyright © 2014 Liangliang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness.

1. Introduction

As many optimization problems cannot be solved by the traditional mathematical programming methods, the metaheuristic algorithms have been widely used to obtain global optimum solutions. And the aim of developing modern metaheuristic algorithms is to increase the accessibility of the global optimum. Inspired by nature, many successful algorithms are proposed, for example, Genetic Algorithm (GA) [1], Particle Swarm Optimization (PSO) [2, 3], Ant Colony Optimization (ACO) [4], Differential Evolution (DE) [5], Harmony Search (HS) [6], Artificial Bee Colony Optimization (ABC) [7], Firefly Algorithm (FA) [8], Artificial Fish Swarm Algorithm (AFSA) [9], Cuckoo Search (CS) [10, 11], Monkey Algorithm (MA) [12], Bat Algorithm (BA) [13], Charged System Search (CSS) [14], and Flower Pollination Algorithm (FPA) [15]. Nature-inspired algorithms can effectively solve the problems which traditional methods cannot solve and have shown excellent performance in many respects. So its application scope has been greatly expanded. In recent years, the metaheuristic algorithms mentioned above have been applied to solve the application problems. For example, Xu et al. (2010) solve the UCAV path planning problems by chaotic artificial bee colony approach [16]. Hasançebi et al. (2013) applied the bat algorithm in structural optimization problems [17]. Askarzadeh (2013) developed a discrete harmony search algorithm for size optimization of wind-photovoltaic hybrid energy system [18]. Basu and Chowdhury (2013) used cuckoo search algorithm in economic dispatch [19].

Based on the simulation of the herding behavior of krill individuals, Gandomi and Alavi proposed the krill herd algorithm (KH) in 2012 [20]. And KH algorithm is a novel biologically inspired algorithm to solve the optimization problems. In KH, the time-dependent position of the krill individuals is formulated by three main factors: motion induced by the presence of other individuals; foraging motion; physical diffusion. Only time interval () should be fine-tuned in the KH algorithm which is a remarkable advantage in comparison with other nature-inspired algorithms. Therefore, it can be efficient for many optimization and engineering problems. To improve the krill herd algorithm, Wang and Guo (2013) proposed a hybrid krill herd algorithm with differential evolution for global numerical optimization [21]. The introduced HDE operator let the krill perform local search within the defined region. And the optimization performance of the DEKH was better than the KH. Then, in order to accelerate convergence speed, thus making the approach more feasible for a wider range of real-world engineering applications while keeping the desirable characteristics of the original KH, an effective Lévy-Flight KH (LKH) method was proposed by Wang et al. in 2013 [22]. And Wang also proposed a new improved metaheuristic simulated annealing-based krill herd (SKH) method for global optimization tasks [23]. The KH algorithm has been applied to solve some application problems. In 2014, a discrete Krill Herd Algorithm was proposed for graph based network route optimization by Sur [24]. KH algorithm was further validated against various engineering optimization problems by Gandomi et al. [25]. Inspired from the animals’ behavior, free search (FS) [26] is firstly proposed by Penev and Littlefair. In FS, each animal has original peculiarities called sense and mobility. And each animal can operate either with small precise steps for local search or with large steps for global exploration. Moreover, the individual decides how to search personally.

In order to overcome the limited performance of standard KH on complex problems, a novel free search krill herd algorithm is proposed in this paper. The free search strategy has been introduced into the standard KH to avoid all krill individuals getting trapped into the local optima. The proposed algorithm can greatly enrich the diversity of krill population and improve the calculation accuracy, which leads to a good optimization performance. What is more, the new method can enhance the quality of solutions without losing the robustness.

The proposed FSKH algorithm is different from standard KH in two aspects. Firstly, in FSKH, the population of individuals is initialized using opposition based learning (OBL) strategy [27]. By using OBL strategy, the proposed algorithm can make a more uniform distribution of the krill populations. What is more, we can obtain fitter starting candidate solutions even when there is no knowledge about the solutions.

And secondly, the krill can do freedom and uncertain action using free search strategy. In standard KH, krill is influenced by its “neighbors” and the optimal krill, and the sensing distance of each krill is fixed. But in nature, even for the same krill, its sensitivity and range of activities will also change in different environment and different period. The proposed algorithm can simulate this freedom, uncertain individual behavior of the krill. The free search strategy allows nonzero probability for access to any location of the search space and highly encourages the individuals to escape from trapping in local optimal solution.

The remainder of this paper is organized as follows. In the Section 2, the standard krill herd algorithm and free search strategy are described, respectively. In Section 3, the concept of opposition based learning (OBL) strategy is briefly explained. And the proposed algorithm (FSKH) is described in detail. The simulation experiments of the proposed algorithm are presented in Section 4, compared to PSO, DE, KH, HS, FS and BA algorithms. Finally, some remarks and conclusions are provided in Section 5.

2. Preliminary

2.1. Krill Herd Algorithm

Krill herd (KH) is a novel metaheuristic swarm intelligence optimization method for solving optimization problems, which is based on the simulation of the herding behavior of krill individuals. The time-dependent position of an individual krill in two-dimensional surface is determined by the following three main actions:(1)movement induced by other krill individuals;(2)foraging activity;(3)physical diffusion.

KH algorithm used the Lagrangian model as follows: where is the motion induced by other krill individuals; is the foraging motion; and is the physical diffusion of the th krill individuals.

2.1.1. Motion Induced by Other Krill Individuals

For a krill individual, the motion induced by other krill individuals can be determined as follows: where is the maximum induced speed, is the inertia weight of the motion induced in , is the last motion induced, is the local effect provided by the neighbors, and is the target direction effect provided by the best krill individual. And the effect of the neighbors can be defined as where is the fitness value of the th krill individual. and are the best and worst fitness values of the krill individuals so far. is the fitness of th () neighbor. And is the number of the neighbors. represents the related positions.

The sensing distance for each krill individual is determined as follows: where is the number of the krill individuals.

The effect of the individual krill with the best fitness on the th individual krill is taken into account using where the value of is defined as

2.1.2. Foraging Motion

The foraging motion is formulated in terms of two main effective parameters. The first is the food location and the second one is the previous experience about the food location. This motion can be expressed for the th krill individual as follows: where is the foraging speed, is the inertia weight of the foraging motion between and , and is the last foraging motion. is the attraction of the food and is the effect of the best fitness of the th krill so far. In our paper, we set .

In KH, the virtual center of food concentration is approximately calculated according to the fitness distribution of the krill individuals, which is inspired from “center of mass.” The center of food for each iteration is formulated as follows: Therefore, the food attraction for the th krill individual can be determined as follows: where is the food coefficient defined as follows: The effect of the best fitness of the th krill individual is also handled using the following equation: where is the best previously visited position of the th krill individual.

2.1.3. Physical Diffusion

The random diffusion of the krill individuals can be considered to be a random process in essence. This motion can be described in terms of a maximum diffusion speed and a random directional vector. It can be indicated as follows: where is the maximum diffusion speed, and is the random directional vector, and its arrays are random values in . The better the position is, the less random the motion is. The effects of the motion induced by other krill individuals and foraging motion gradually decrease with increasing the time (iterations). Thus, another term equation (12) into equation (13). This term linearly decreases the random speed with the time and performs on the basis of a geometrical annealing schedule:

2.1.4. Crossover

The crossover is controlled by a crossover probability , and the th component of (i.e., ) is defined as follows: where .

2.1.5. Mutation

The mutation is controlled by a mutation probability , and the adaptive mutation scheme is determined as follows: where , .

2.1.6. Main Procedure of the Krill Herd Algorithm

In general, the defined motions frequently change the position of a krill individual toward the best fitness. The foraging motion and the motion induced by other krill individuals contain two global and two local strategies. These are working in parallel which make KH a powerful algorithm. Using different effective parameters of the motion during the time, the position vector of a krill individual during the interval to is given by the following equation:

It should be noted that is one of the most important constants and should be carefully set according to the optimization problem. This is because this parameter works as a scale factor of the speed vector. can be simply obtained from the following formula: where is the total number of variables and , are lower and upper bounds of theth variables , respectively. is a constant number between (Algorithm 1).

alg1
Algorithm 1: Krill herd algorithm.

2.2. Free Search Operator

Free search (FS) [26] models the behavior of animals and operates on a set of solutions called population. In the algorithm each animal has original peculiarities called sense and mobility. The sense is an ability of the animal for orientation within the search space. The animal uses its sense for selection of location for the next step. Different animals can have different sensibilities. It also varies during the optimization process, and one animal can have different sensibilities before different walks. The animal can select, for start of the exploration walk, any location marked with pheromone, which fits its sense. During the exploration walk the animals step within the neighbor space. The neighbor space also varies for the different animals. Therefore, the probability for access to any location of the search space is nonzero.

During the exploration, each krill achieves some favor (an objective function solution) and distributes a pheromone in amount proportional to the amount of the found favor (the quality of the solution). The pheromone is fully replaced with a new one after each walk.

Particularly, the animals in the algorithm are mobile. Each animal can operate either with small precise steps for local search or with large steps for global exploration. And each animal decides how to search (with small or with large steps) by itself. Explicit restrictions do not exist. The previous experience can be taken into account, but it is not compulsory.

2.2.1. The Structure of the FS Operator

The structure of the algorithm consists of three major events: initialization, exploration, and termination.

(1) Initialization. In this paper, the initialization strategy is , where is a random value in and , are the space borders.

(2) Exploration. The exploration walk generates coordinates of a new location as

The modification strategy is where is step limit per walk and is current step.

The individual behavior, during the walk, is modeled and described as , , where is the only location marked with pheromone from an animal after the walk.

The pheromone generation is

The sensibility generation is where and are minimal and maximal possible values of the sensibility. and are minimal and maximal possible values of the pheromone trials. And , .

Selection and decision making for a start location for an exploration walk is where ,  , and is the marked locations number.

(3) Termination. In this paper, the criterion for termination is , where is the maximum number of iterations.

The steps are imitation of the ability for motion and action. The steps can be large or small and can vary. In the search process, the neighbor space is a tool for tuning rough and precise searches. So, search radius is a parameter related to individual search space; the value of decides the optimization quality during the search process.

There are two methods to set the value of search radius. The first one is that is constant. If the value is higher, the individual search space is wider, search time is longer, and the convergence precision is lower. If the value is lower, the individual search space is smaller and the convergence precision is higher.

The second method is that changing neighbor space adaptively. And during the search process, is decreasing gradually. The rule is as follows: where is the exploration generation and is the radius contract coefficient, which is an important parameter. In this paper, we adopt the first approach.

2.2.2. Procedure of FS Operator

The detailed process of FS operator is described as in Algorithm 2.

alg2
Algorithm 2: Free search operator.

3. Free Search krill Herd Algorithm

In KH algorithm, krill is influenced by its “neighbors” and the optimal krill, and the sensing distance of each krill is fixed. But in nature, the action of each krill is free and uncertain. In order to simulate this freedom, uncertain individual behavior of the krill, this paper introduces the free search strategy into the krill herd algorithm and proposes a novel free search krill herd algorithm (FSKH).

3.1. Opposition-Based Population Initialization

Population initialization has an important impact on the optimization results and global convergence; this paper introduces the initialization method of opposition based learning (OBL) strategy [27] to generate initial krill populations (Algorithm 3).

alg3
Algorithm 3: Opposition-based population initialization.

By utilizing OBL we can obtain fitter starting candidate solutions even when there is no knowledge about the solutions. This initialization method can make a more uniform distribution of the krill populations. Therefore, it is good for the method to get better optimization results. And by utilizing free search strategy, each krill individual in FSKH can decide how to search by itself (Algorithm 4). The strategy allows nonzero probability to approach to any location of the search space and highly encourages the individuals to escape from trapping in local optimal solution. During the search process, each krill takes exploration walks according to different search radius.

alg4
Algorithm 4: Free search krill herd algorithm.

In general, three main actions in standard KH algorithm can guide the krill individuals to search the promising solution space. But it is easy for the standard KH algorithm to be trapped into local optima, and the performance in high-dimensional cases is unsatisfied. In the FSKH algorithm, the individual can search the promising area with small or large steps. So, the krill individuals can move step by step through multidimensional search space. In nature, the activity range of krill individuals is different. can adjust the activity range of the individual, and there is no explicit restrictions.

Using the free search strategy, the krill individual can search any region of the search space. Each krill individual can search according to their perception and the scope of activities and can not only search around the global optimum, but also search around local optimum. When using larger step, it takes global search which can strengthen the weak global search ability of KH. Therefore, the proposed algorithm has better population diversity and convergence speed and can enhance the global searching ability of the algorithm. To achieve a better balance between local search and global search, FSKH algorithm includes both “exploration” process of FS and “exploitation” process of KH. When increasing the sensitivity, the krill individual will approach the whole population’s current best value (i.e., local search). While reducing the sensitivity, the krill individual can search around other neighborhood (i.e., global search) (Figure 1).

936374.fig.001
Figure 1: Flowchart of the free search krill algorithm.

4. Simulation Experiments

4.1. Simulation Platform

All the algorithms compared in this section are implemented in Matlab R2012a (7.14). And experiments are performed on a PC with a 3.01 GHz, AMD Athlon (tm) II X4 640 Processor, 3 GB of RAM, and Windows XP operating system. In the tests, population size is . The experiment results are obtained in 50 trials.

4.2. Benchmark Functions

In order to verify the effectiveness of the proposed algorithm, we select 14 standard benchmark functions [2830] to detect the searching capability of the proposed algorithm. The proposed algorithm in this paper (i.e., FSKH) is compared with PSO, DE, KH, HS, FS, and BA.

4.3. Parameter Setting

Generally, the choice of parameters requires some experimenting. In this paper, after a lot of experimental comparison, the parameters of the algorithm are set as follows.

In KH and FSKH, the maximum induced speed , the foraging speed , and the maximum diffusion speed . In FSKH and FS, the search radius But in the FSKH, the search step is . In FS, the search step is .

In BA, the parameters are generally set as follows: pulse frequency range is , the maximum loudness is , maximum pulse emission is , attenuation coefficient of loudness is , and increasing coefficient of pulse emission is . In PSO, we use linear decreasing inertia weight , and learning factor is . In HS, the harmony consideration rate is , the minimum pitch adjusting rate is , the maximum pitch adjusting rate is , the minimum bandwidth is , and the maximum bandwidth is . In DE, the crossover constant is .

4.4. Comparison of Experiment Results

The best, mean, worst, and Std. represent the optimal fitness value, mean fitness value, worst fitness value, and standard deviation, respectively. Bold and italicized results mean that FSKH is better, while the * results means that other algorithm is better.

For the low-dimensional case, the maximum number of iterations of each algorithm is . As seen from Tables 2 and 3, FSKH provides better results than other algorithms except , , and . What is more, FSKH can find the theoretical optimum solutions for nine benchmark functions (, , , ) and has a very strong robustness. For other algorithms, only PSO can find the theoretical optimum solution for three functions (,,), DE can find the theoretical optimum solution for one function (), and KH II can find the theoretical optimum solution for one function (). The number of finding the optimal solution for FSKH is more than that of the other six algorithms. For and FSKH has a higher precision of optimization. The accuracy of FSKH can be higher than that of other algorithms for 2 and 14 orders of magnitude, respectively, at least. For , , and , we can find that there is at least one algorithm that can perform better than FSKH. But in general, even for these three functions, the performance of FSKH is highly competitive with other algorithms. For all functions, the standard deviations of FSKH are very small are very small which indicates that FSKH is very robust and efficient.

For benchmark functions in Table 1, Figures 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15 are the convergence curves, Figures 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, and 29 are the ANOVA tests of the global minimum. Figures 2, 3, 4, 5, 6, 7, 8, 9, and 11 show that the convergence speed of FSKH is quicker than other algorithms. FSKH is the best algorithm for most functions. Moreover, as seen from the ANOVA tests of the global minimum, we can find that FSKH is the most robust method. For the fourteen functions, other algorithms compared (i.e., PSO, DE, KH, HS, FS, and BA) can only be robust for a few functions, but cannot be robust for all functions. Therefore, FSKH is an effective and feasible method for optimization problems in low-dimensional case.

tab1
Table 1: Benchmark functions.
tab2
Table 2: Simulation results for in low dimension.
tab3
Table 3: Simulation results for .
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Figure 2: Convergence curves for .
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Figure 3: Convergence curves for
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Figure 4: Convergence curves for .
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Figure 5: Convergence curves for .
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Figure 6: Convergence curves for .
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Figure 7: Convergence curves for .
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Figure 8: Convergence curves for .
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Figure 9: Convergence curves for .
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Figure 10: Convergence curves for .
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Figure 11: Convergence curves for .
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Figure 12: Convergence curves for .
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Figure 13: Convergence curves for .
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Figure 14: Convergence curves for .
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Figure 15: Convergence curves for .
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Figure 16: ANOVA tests of the global minimum for .
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Figure 17: ANOVA tests of the global minimum for .
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Figure 18: ANOVA tests of the global minimum for .
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Figure 19: ANOVA tests of the global minimum for .
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Figure 20: ANOVA tests of the global minimum for .
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Figure 21: ANOVA tests of the global minimum for .
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Figure 22: ANOVA tests of the global minimum for .
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Figure 23: ANOVA tests of the global minimum for .
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Figure 24: ANOVA tests of the global minimum for .
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Figure 25: ANOVA tests of the global minimum for .
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Figure 26: ANOVA tests of the global minimum for .
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Figure 27: ANOVA tests of the global minimum for .
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Figure 28: ANOVA tests of the global minimum for .
936374.fig.0029
Figure 29: ANOVA tests of the global minimum for .

In order to validate the optimization ability of algorithms in high-dimensional case, this paper set and to 100-dimension, and to 200-dimension, and to 300-dimension, and to 500-dimension, and to 1000-dimension. The maximum number of iterations of each algorithm is adjusted to , and other parameters are the same.

The comparison results for high-dimensional case are shown in Table 4. As seen from the results, the optimization performance of FSKH is the best. Although the dimension of the functions is very high, the proposed algorithm (FSKH) can also find optimum solutions for six benchmark functions (, , , ). Some algorithms can show a good performance in low-dimensional case, but in the high-dimensional case it cannot get a good result for most benchmark functions. The optimization ability of FSKH does not show a significant decline with increasing the dimension.

tab4
Table 4: Simulation results for in high dimension.

Figures 30, 31, 32, 33, 34, 35, 36, 37, 38, and 39 are the convergence curves for high-dimensional case and Figures 40, 41, 42, 43, 44, 45, 46, 47, 48, and 49 are the ANOVA tests of the global minimum for high-dimensional case. As seen from the ANOVA tests of the global minimum, we can find that FSKH is still the most robust method, and superiority is even more obvious. Therefore, in high-dimensional case, FSKH can also be an effective and feasible method for optimization problems. For metaheuristic algorithm, it is important to tune its related parameters. The proposed algorithm is not a very complex metaheuristic algorithm. Only time interval () and search radius () should be fine-tuned in FSKH. It is a remarkable advantage of the proposed algorithm compared with other algorithms. But in order to get high accuracy solutions, the time complexity of the proposed algorithm is a little high. How to reduce the time complexity of the proposed algorithm by some strategies is our main work in the future.

936374.fig.0030
Figure 30: Convergence curves for .
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Figure 31: Convergence curves for .
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Figure 32: Convergence curves for .
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Figure 33: Convergence curves for .
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Figure 34: Convergence curves for .
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Figure 35: Convergence curves for .
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Figure 36: Convergence curves for .
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Figure 37: Convergence curves for .
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Figure 38: Convergence curves for .
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Figure 39: Convergence curves for .
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Figure 40: ANOVA tests of the global minimum for .
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Figure 41: ANOVA tests of the global minimum for .
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Figure 42: ANOVA tests of the global minimum for .
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Figure 43: ANOVA tests of the global minimum for .
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Figure 44: ANOVA tests of the global minimum for .
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Figure 45: ANOVA tests of the global minimum for .
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Figure 46: ANOVA tests of the global minimum for .
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Figure 47: ANOVA tests of the global minimum for .
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Figure 48: ANOVA tests of the global minimum for .
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Figure 49: ANOVA tests of the global minimum for .

5. Conclusions

In order to overcome the shortcomings of krill herd algorithm (e.g., poor population diversity, low precision of optimization, and poor optimization performance in high dimensional case). This paper introduces the free search strategy into krill herd algorithm and proposes a novel free search krill herd algorithm (FSKH). The main improvement is that the krill individual can search freely and the diversity of krill population is enriched. The proposed algorithm (FSKH) achieves a better balance between the global search and local search. Experiment simulation and comparison results with other algorithms show that the optimization precision, convergence speed, and robustness of FSKH are all better than other algorithms for most benchmark functions. So FSKH is a more feasible and effective way for optimization problems.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work is supported by the National Science Foundation of China under Grant no. 61165015, the Key Project of Guangxi Science Foundation under Grant no. 2012GXNSFDA053028, the Key Project of Guangxi High School Science Foundation under Grant no. 20121ZD008, and the Funded by Open Research Fund Program of Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China under Grant no. IPIU01201100.

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