HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.     

Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Neural Computing and Applications - Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model...     

多峰值全局优化的混合算法

根据遗传算法在较小的可行区域内一般有较好的求解结果这一事实,提出了一种混合算法。该算法先利用区间算法求解全局优化问题来得到包含所有最优解的小区间,随后运用遗传算法进行后续过程。算法能够有效缩小一个较大的可行区域空间,提供高适应值的初始种群,求出多峰值问题的全部最优解,提高算法的求解精度同时避免陷入局部最优。最后数值实验说明了算法的有效性。     

Hybrid parallel chaos optimization algorithm with harmony search algorithm

The application of chaotic sequences can be an interesting alternative to provide search diversity in an optimization procedure, named chaos optimization algorithm (COA). Since the chaotic motion is pseudo-randomness and chaotic sequences are sensitive to the initial conditions, the search ability of COA is usually effected by the starting values. Considering this weakness, parallel chaos optimization algorithm (PCOA) is studied in this paper. To obtain optimum solution accurately, harmony search algorithm (HSA) is integrated with PCOA to form a novel hybrid algorithm. Different chaotic maps are compared and the impacts of parallel parameter on the hybrid algorithm are discussed. Several simulation results are used to show the effective performance of the proposed hybrid algorithm.     

Hybrid Taguchi-genetic algorithm for global numerical optimization

In this paper, a hybrid Taguchi-genetic algorithm (HTGA) is proposed to solve global numerical optimization problems with continuous variables. The HTGA combines the traditional genetic algorithm (TGA), which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimum offspring. The Taguchi method is inserted between crossover and mutation operations of a TGA. Then, the systematic reasoning ability of the Taguchi method is incorporated in the crossover operations to select the better genes to achieve crossover, and consequently, enhance the genetic algorithm. Therefore, the HTGA can be more robust, statistically sound, and quickly convergent. The proposed HTGA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions and very large numbers of local minima. The computational experiments show that the proposed HTGA not only can find optimal or close-to-optimal solutions but also can obtain both better and more robust results than the existing algorithm reported recently in the literature.     

Dynamic partition search algorithm for global numerical optimization

This paper presents a novel evolutionary algorithm entitled Dynamic Partition Search Algorithm (DPSA) for global optimization problems with continuous variables. The DPSA is a population-based stochastic search algorithm in nature, which mainly consists of initialization process and evolution process. In the initialization process, the DPSA randomly generates an initial population of members from a specific search space and finds a leader. In the evolution process, the DPSA applies two groups to balance exploration ability and exploitation ability, in which one group is in charge of exploring new region via a dynamic partition strategy, and the other group relies on Cauchy distributions to exploit the region around the best member. Later, numerical experiments are conducted for 24 classical benchmark functions with 100, 1000 or even 10000 dimensions. And the performance of the proposed DPSA is compared with a state-of-the-art cooperative coevolving particle swarm optimization (CCPSO2), and two existing differential evolution (DE) algorithms. The experimental results show that DPSA has a better performance than the algorithms used for comparison, especially for high dimensional optimization problems. In addition, the numerical computational results also demonstrate that the DPSA has good scalability, and it is an effective evolutionary algorithm for solving large-scale global optimization problems.     

The spherical search algorithm for bound-constrained global optimization problems

In this paper, a new optimization algorithm called Spherical Search (SS) is proposed to solve the bound-constrained non-linear global optimization problems. The main operations of SS are the calculation of spherical boundary and generation of new trial solution on the surface of the spherical boundary. These operations are mathematically modeled with some more basic level operators: Initialization of solution, greedy selection and parameter adaptation, and are employed on the 30 black-box bound constrained global optimization problems. This study also analyzes the applicability of the proposed algorithm on a set of real-life optimization problems. Meanwhile, to show the robustness and proficiency of SS, the obtained results of the proposed algorithm are compared with the results of other well-known optimization algorithms and their advanced variants: Particle Swarm Optimization (PSO), Differential Evolution (DE), and Covariance Matrix Adapted Evolution Strategy (CMA-ES). The comparative analysis reveals that the performance of SS is quite competitive with respect to the other peer algorithms.     

基于Powell局部搜索策略的全局优化布谷鸟算法

为了解决布谷鸟搜索算法后期收敛速度慢、求解精度不高、易陷入局部最优等缺陷,提出了一种基于Powell局部搜索策略的全局优化布谷鸟搜索算法.算法将布谷鸟全局搜索能力与Powell方法的局部寻优性能有机地结合,并根据适应度值逐步构建精英种群候选解池在迭代后期牵引Powell搜索的局部优化,在保证求解速度、尽可能找到全局极值点的同时提高算法的求解精度.对52个典型测试函数实验结果表明,该算法相比于传统的布谷鸟搜索算法不仅寻优精度和寻优率有所提高,并且适应能力强、鲁棒性好,与最新提出的其他改进算法相比也具有一定的竞争优势.     

Hybrid krill herd algorithm with differential evolution for global numerical optimization

In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid differential evolution KH (DEKH) method has been developed for function optimization. The improvement involves adding a new hybrid differential evolution (HDE) operator into the krill, updating process for the purpose of dealing with optimization problems more efficiently. The introduced HDE operator inspires the intensification and lets the krill perform local search within the defined region. DEKH is validated by 26 functions. From the results, the proposed methods are able to find more accurate solution than the KH and other methods. In addition, the robustness of the DEKH algorithm and the influence of the initial population size on convergence and performance are investigated by a series of experiments.     

求解全局优化问题的混合人工鱼群算法

把Powell算法作为人工鱼群算法的一个局部搜索算子,嵌入到自适应人工鱼群算法中,构成一种基于Powell算法和自适应人工鱼群的混合算法。该算法充分利用了自适应人工鱼群算法的全局收敛性和Powell算法的强局部搜索能力,使得混合算法的全局收敛性能得到了改善,并且减少了计算量。计算机仿真结果表明,自适应混合人工鱼群算法能够在保持较高精度的前提下快速收敛。     

A stochastic local search algorithm for constrained continuous global optimization

This paper presents a new stochastic local search algorithm known as feasible–infeasible search procedure (FISP) for constrained continuous global optimization. The proposed procedure uses metaheuristic strategies for combinatorial optimization as well as combined strategies for exploring continuous spaces, which are applied to an efficient process in increasingly refined neighborhoods of current points. We show effectiveness and efficiency of the proposed procedure on a standard set of 13 well‐known test problems. Furthermore, we compare the performance of FISP with SNOPT (sparse nonlinear optimizer) and with few successful existing stochastic algorithms on the same set of test problems.     

An effective hybrid cuckoo search algorithm for constrained global optimization

In recognition of high-quality wideband speech codecs, several standardization activities have been conducted, resulting in the selection of a wideband speech codec called adaptive multi-rate wideband (AMR-WB). The algebraic code-excited linear prediction (ACELP) technique is recommended in AMR-WB, and it is noted that most of the complexity in the ACELP structure comes from the codebook search. In this paper, a new method is proposed for codebook search based on the behavior of backward filtered target signal, d(n), introduced in ITU-T G.722.2 recommendation. To optimize the proposed scheme, five optimization algorithms (i.e., modified genetic algorithm, particle swarm optimization with dynamic inertia weight, bee colony optimization, modified differential evolution, and imperialist competition algorithm) are investigated. Experimental results show that the reduction in codebook search operations of the proposed method is able to reach up to 59 percent as compared with ITU-T G.722.2 recommendation. Meanwhile, BCO-based codebook search scheme has better convergence speed without significant degradation in quality metrics, such as segmental signal-to-noise ratio, mean opinion score, and perceptual evaluation of speech quality, when used in an AMR-WB speech codec.     

A new improved whale optimization algorithm with joint search mechanisms for high-dimensional global optimization problems

Similar to other swarm-based algorithms, the recently developed whale optimization algorithm (WOA) has the problems of low accuracy and slow convergence. It is also easy to fall into local optimum. Moreover, WOA and its variants cannot perform well enough in solving high-dimensional optimization problems. This paper puts forward a new improved WOA with joint search mechanisms called JSWOA for solving the above disadvantages. First, the improved algorithm uses tent chaotic map to maintain the diversity of the initial population for global search. Second, a new adaptive inertia weight is given to improve the convergence accuracy and speed, together with jump out from local optimum. Finally, to enhance the quality and diversity of the whale population, as well as increase the probability of obtaining global optimal solution, opposition-based learning mechanism is used to update the individuals of the whale population continuously during each iteration process. The performance of the proposed JSWOA is tested by twenty-three benchmark functions of various types and dimensions. Then, the results are compared with the basic WOA, several variants of WOA and other swarm-based intelligent algorithms. The experimental results show that the proposed JSWOA algorithm with multi-mechanisms is superior to WOA and the other state-of-the-art algorithms in the competition, exhibiting remarkable advantages in the solution accuracy and convergence speed. It is also suitable for dealing with high-dimensional global optimization problems.

     

An interval arithmetic method for global optimization

An interval arithmetic method is described for finding the global maxima or minima of multivariable functions. The original domain of variables is divided successively, and the lower and the upper bounds of the interval expression of the function are estimated on each subregion. By discarding subregions where the global solution can not exist, one can always find the solution with rigorous error bounds. The convergence can be made fast by Newton's method after subregions are grouped. Further, constrained optimization can be treated using a special transformation or the Lagrange-multiplier technique.     

Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data

Gene expression data play a significant role in the development of effective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively affect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid filter/wrapper gene selection method, called mRMR-BAOAC-SA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a first-stage filter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classification purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classification accuracy and the minimum number of selected genes.

     

An exact penalty function-based differential search algorithm for constrained global optimization

Differential search (DS) is a recently developed derivative-free global heuristic optimization algorithm for solving unconstrained optimization problems. In this paper, by applying the idea of exact penalty function approach, a DS algorithm, where an S-type dynamical penalty factor is introduced so as to achieve a better balance between exploration and exploitation, is developed for constrained global optimization problems. To illustrate the applicability and effectiveness of the proposed approach, a comparison study is carried out by applying the proposed algorithm and other widely used evolutionary methods on 24 benchmark problems. The results obtained clearly indicate that the proposed method is more effective and efficient over the other widely used evolutionary methods for most these benchmark problems.     

Artificial bee colony algorithm and pattern search hybridized for global optimization

Artificial bee colony algorithm is one of the most recently proposed swarm intelligence based optimization algorithm. A memetic algorithm which combines Hooke–Jeeves pattern search with artificial bee colony algorithm is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the exploration phase realized by artificial bee colony algorithm and the exploitation phase completed by pattern search. The proposed algorithm was tested on a comprehensive set of benchmark functions, encompassing a wide range of dimensionality. Results show that the new algorithm is promising in terms of convergence speed, solution accuracy and success rate. The performance of artificial bee colony algorithm is much improved by introducing a pattern search method, especially in handling functions having narrow curving valley, functions with high eccentric ellipse and some complex multimodal functions.     

具有拓扑时变和搜索扰动的混合粒子群优化算法

粒子群优化（PSO）算法在求解复杂多峰函数时极易早熟,陷入局部最优无法跳出。研究表明改变粒子间的拓扑结构和调整算法的迭代机制有助于改善种群的多样性,提高算法的寻优能力。因此,提出一种具有拓扑时变和搜索扰动的混合粒子群优化（HPSO-TS）算法。该算法采用K-medoids聚类算法对粒子群进行动态分簇,形成多个异构子群,以利于子群内粒子间进行信息流通。在速度更新中,增加簇最优粒子的引导,并引入非线性变化极值扰动,帮助粒子搜索更多的区域。而后在位置迭代中引入花授粉算法（FPA）中的转换概率,使粒子在全局搜索和局部搜索之间转换。在全局搜索时结合狮群算法中的母狮觅食机制对粒子的位置进行更新;在局部搜索时引入正弦扰动因子,帮助粒子跳出局部最优。实验结果表明所提算法在求解精度和鲁棒性方面明显优于FPA、PSO、改进粒子群算法（IPSO）、具有动态拓扑结构的粒子群算法（PSO-T）;并且随着测试维度和次数的增加,这种优势更加明显。HPSO-TS算法所引入的拓扑时变策略和搜索扰动机制能有效地提高种群的多样性和粒子的活性,从而改善寻优能力。     

求解约束化工优化问题的混合布谷鸟搜索算法

针对布谷鸟搜索算法存在收敛速度慢和易陷入局部最优等缺陷,提出一种基于Rosenbrock搜索和柯西变异的混合布谷鸟搜索算法用于求解约束化工优化问题。该算法首先采用佳点集方法对鸟窝位置进行初始化,为全局搜索的多样性奠定基础;然后利用Rosenbrock搜索算法对当前最优位置进行局部搜索,以提高算法的收敛速度;最后对当前最优解进行柯西变异以避免算法陷入局部最优。两个约束化工优化问题的实验结果表明了该混合算法的有效性。