Hybrid genetic algorithm with adaptive local search scheme

This paper proposes a hybrid genetic algorithm (a-hGA) with adaptive local search scheme. For designing the a-hGA, a local search technique is incorporated in the loop of genetic algorithm (GA), and whether or not the local search technique is used in the GA is automatically determined by the adaptive local search scheme. Two modes of adaptive local search schemes are developed in this paper. First mode is to use the conditional local search method that can measure the average fitness values obtained from the continuous two generations of the a-hGA, while second one is to apply the similarity coefficient method that can measure a similarity among the individuals of the population of the a-hGA. These two adaptive local search schemes are included in the a-hGA loop, respectively. Therefore, the a-hGA can be divided into two types: a-hGA1 and a-hGA2. To prove the efficiency of the a-hGA1 and a-hGA2, a canonical GA (cGA) and a hybrid GA (hGA) with local search technique and without any adaptive local search scheme are also presented. In numerical example, all the algorithms (cGA, hGA, a-hGA1 and a-hGA2) are tested and analyzed. Finally, the efficiency of the proposed a-hGA1 and a-hGA2 is proved by various measures of performance.     

On a cuckoo search optimization approach towards feedback system identification

This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR modeling mechanisms. The performance of the new method has been compared with that obtained by other evolutionary computing algorithms like genetic algorithm (GA) and particle swarm optimization (PSO). The superior system identification capability of the proposed scheme is evident from the results obtained through an exhaustive simulation study.     

Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics

A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of offspring. The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring. This algorithm is successfully applied to general WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms.     

Hybrid genetic algorithm approach for precedence-constrained sequencing problem

The objective of precedence-constrained sequencing problem (PCSP) is to locate the optimal sequence with the shortest traveling time among all feasible sequences. Various methods for effectively solving the PCSP have been suggested. This paper proposes a new concept of hybrid genetic algorithm (HGA) with adaptive local search scheme in order that the PCSP should be effectively solved. By the use of the adaptive local search scheme, the local search is automatically adapted into the loop of genetic algorithm. Two types of the PCSP are presented and analyzed to compare the efficiency among the proposed HGA approach and other competing conventional approaches. Finally, it is proved that the proposed HGA approach outperforms the other competing conventional approaches.     

Many-objective evolutionary algorithm based on adaptive weighted decomposition

Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts.     

基于模拟退火的混合差分进化算法及其在联合补货-配送集成优化中的应用

针对差分进化 (Differential evolution, DE)算法搜索效率较低和容易陷入局部最优的缺点,设计了基于SA的混合差分进化算法(SA-based Hybrid DE, SAHDE),以提高DE算法的全局寻优能力。该算法采用自适应变异算子和交叉算子,并结合模拟退火(Simulated Annealing, SA)算法的Metropolis 准则。首先通过标准测试函数对改进的SAHDE进行性能测试,证明了该算法比DE、自适应混合DE (Adaptive Hybrid DE, AHDE)和遗传算法(Genetic Algorithm, GA)更有效。进而将该算法运用到联合补货-配送集成优化(典型NP-hard)问题的求解中,通过大规模的算例分析,证实SAHDE在解决联合补货-配送优化问题比DE、AHDE和GA更有效。     

基于多种群的随机扰动蚁群算法求解分布式约束优化问题

针对现有的基于蚁群优化思想求解分布式约束优化问题的算法收敛较慢、容易陷入局部最优等问题,提出了一种基于多种群的随机扰动蚁群算法（random disturbance based multi-population ant colony algorithm to solve distributed constraint optimization problems,RDMAD）来求解分布式约束优化问题。首先,RDMAD提出了一种分工合作机制,将种群按比例划分为采用贪婪搜索的子种群和采用启发式搜索的子种群,同时构建分级更新策略,提高算法收敛速度和求解质量;然后对采用贪婪搜索的子种群设计自适应变异算子和奖惩机制,防止算法陷入局部最优;最后在算法陷入停滞时触发随机扰动策略,增加种群多样性。将RDMAD与七种最先进的非完备算法在三类基准问题上的寻优结果进行了实验对比,实验结果表明RDMAD在求解质量和收敛速度上优势明显,且稳定性较高。     

Combinations of estimation of distribution algorithms and other techniques

This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems:a) guided mutation,an offspring generator in which the ideas from EDAs and genetic algorithms are combined together,we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique problem,b)evolutionary algorithms refining a heuristic,we advocate a strategy for solving a hard optimization problem with complicated data structure,and c) combination of two different local search techniques and EDA for numerical global optimization problems,its basic idea is that not all the new generated points are needed to be improved by an expensive local search.     

Accelerating Differential Evolution Using an Adaptive Local Search

We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.     

自适应动态邻域布谷鸟混合算法求解TSP问题

针对离散布谷鸟算法求解旅行商问题时邻域搜索效率低和易陷入局部最优解等问题,提出了一种自适应动态邻域布谷鸟混合算法（Adaptive Dynamic Neighborhood Hybrid Cuckoo Search algorithm,ADNHCS）。为了提升邻域搜索效率,设计了一种圆限定突变的动态邻域结构来降低经典算法的随机性;此外,提出了可根据迭代过程进行自适应参数调整的策略,并结合禁忌搜索算法来提升全局寻优的能力。使用MATLAB和标准TSPLIB数据库中的若干经典算例对算法性能进行了实验仿真,结果表明与其他基于布谷鸟算法、经典和新型群智能优化算法相比,ADNHCS算法在全局寻优能力以及稳定性方面表现更优。     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

基于演化搜索信息的量子行为粒子群优化算法

针对量子行为粒子群优化算法可能过早收敛而陷入局部最优的问题,提出了基于演化搜索信息的非重复访问量子行为粒子群优化算法（Non-revisited QPSO,NrQPSO）。该算法将演化搜索信息记录方案和标准QPSO算法结合起来,确保所有更新的粒子位置都是未被重复访问的,并通过变异操作增加粒子的多样性。演化搜索信息记录方案利用二维空间分割树（BSP）将连续搜索空间划分为不同的重叠子区域,并且将子区域作为粒子变异范围,使得相应的变异操作是一种无参数的自适应变异。对比其他传统算法,通过对八个标准测试函数的实验结果表明,NrQPSO算法在处理多峰和单峰测试函数时具有更好的优化性能,收敛精度和收敛速度都得到了提高,证明该算法的有效性。     

An innovative framework for designing genetic algorithm structures

Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields. Like other meta-heuristic algorithms, Genetic Algorithms can only provide a probabilistic guarantee of the global optimal solution. Having a Genetic Algorithm (GA) capable of finding the global optimal solution with high success probability is always desirable. In this article, an innovative framework for designing an effective GA structure that can enhance the GA's success probability of finding the global optimal solution is proposed. The GA designed with the proposed framework has three innovations. First, the GA is capable of restarting its search process, based on adaptive condition, to jump out of local optima, if being trapped, to enhance the GA's exploration. Second, the GA has a local solution generation module which is integrated in the GA loop to enhance the GA's exploitation. Third, a systematic method based on Taguchi Experimental Design is proposed to tune the GA parameter set to balance the exploration and exploitation to enhance the GA capability of finding the global optimal solution. Effectiveness of the proposed framework is validated in 20 large-scale case study problems in which the GA designed by the proposed framework always outperforms five other algorithms available in the global optimization literature.     

Competitive Hopfield Network Combined With Estimation of Distribution for Maximum Diversity Problems

This paper presents a discrete competitive Hopfield neural network (HNN) (DCHNN) based on the estimation of distribution algorithm (EDA) for the maximum diversity problem. In order to overcome the local minimum problem of DCHNN, the idea of EDA is combined with DCHNN. Once the network is trapped in local minima, the perturbation based on EDA can generate a new starting point for DCHNN for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. The proposed algorithm is tested on 120 benchmark problems with the size ranging from 100 to 5000. Simulation results show that the proposed algorithm is better than the other improved DCHNN such as multistart DCHNN and DCHNN with random flips and is better than or competitive with metaheuristic algorithms such as tabu-search-based algorithms and greedy randomized adaptive search procedure algorithms.      

具有自适应调整策略的混沌灰狼优化算法

灰狼优化算法(Grey Wolf Optimization,GWO)是新型启元优化算法,相比于其他群体智能优化算法,该算法同样存在收敛速度较慢、不稳定、易陷入局部最优等问题。针对上述问题,根据GWO算法的结构特点,提出了一种自适应调整策略的混沌灰狼优化算法(Chaotic Local Search GWO),利用自适应调整策略来提高GWO算法的收敛速度,通过混沌局部搜索策略增加种群的多样性,使搜索过程避免陷入局部最优。最后利用6个测试函数对算法进行仿真验证,并结合其他4种算法进行了横向比较。实验结果证明,所提出的改进算法在收敛速度、精度以及稳定性方面具有明显的优势。     

A differential memetic algorithm

Memetic algorithms have been devised to rectify the absence of a local search mechanism in evolutionary algorithms. This paper proposes a differential memetic algorithm (DMA). To this end, first we propose a differential bidirectional random search as a local search algorithm. Then, a randomized blending crossover (RBleX) is proposed which aimed to scatter the new born offspring more diversely in the whole search space. We devise our proposed DMA, by using the RBleX crossover in the GA, and including the DBRS local search algorithm. A comparison of the performance of the DMA and those of seven other evolutionary/memetic or hybrid algorithms reported in two different papers on numerous bechmark functions demonstrates better performance of proposed DMA algorithm in most of the cases.     

An Electromagnetism-like algorithm for cell formation and layout problem

Implementation of cellular manufacturing systems (CMS) is thriving among manufacturing companies due to many advantages that are attained by applying this system. In this study CMS formation and layout problems are considered. An Electromagnetism like (EM-like) algorithm is developed to solve the mentioned problems. In addition the required modifications to make EM-like algorithm applicable in these problems are mentioned. A heuristic approach is developed as a local search method to improve the quality of solution of EM-like. Beside in order to examine its performance, it is compared with two other methods. The performance of EM-like algorithm with proposed heuristic and GA are compared and it is demonstrated that implementing EM-like algorithm in this problem can improve the results significantly in comparison with GA. In addition some statistical tests are conducted to find the best performance of EM-like algorithm and GA due to their parameters. The convergence diagrams are plotted for two problems to compare the convergence process of the algorithms. For small size problems the performances of the algorithms are compared with an exact algorithm (Branch & Bound).     

Quantum cooperative search algorithm for 3-SAT

Grover's search algorithm, one of the most popular quantum algorithms, provides a good solution to solve NP complexity problems, but requires a large number of quantum bits (qubits) for its functionality. In this paper, a novel algorithm called quantum cooperative search is proposed to make Grover's search algorithm work on 3-SAT problems with a small number of qubits. The proposed algorithm replaces some qubits with classical bits and finds assignments to these classical bits using the traditional 3-SAT algorithms including evolutionary algorithms and heuristic local search algorithms. In addition, the optimal configuration of the proposed algorithm is suggested by mathematical analysis. The experimental results show that the quantum cooperative search algorithm composed by Grover's search and heuristic local search performs better than other pure traditional 3-SAT algorithms in most cases. The mathematical analysis of the appropriate number of qubits is also verified by the experiments.     

Combining the Filtered Evaluation Function With Coevolutionary Shared Niching in Genetic Algorithms

In this paper, we propose the combination of filtered evaluation and coevolutionary shared niching (CSN) for extending the search ability of genetic algorithms (GA). The proposed scheme can overcome the problems of the filtering GA (FGA) and the CSN. The successful optimization ability of the FGA is supported by the filtered evaluation method that can modify the landscape for escaping local optima. However, the problem of the FGA is the relatively high cost to maintain the filter. The CSN can autonomously maintain the shared distance using the coevolution between two populations (called customers and businessmen). However, the escaping ability from local optima of the CSN is still insufficient. Therefore, the combination of the filtered evaluation and the CSN is proposed, to reduce the cost of the FGA filter. The effectiveness of the proposed scheme is confirmed through test problems.     

An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems

This paper presents an adaptive bi-flight cuckoo search algorithm for continuous dynamic optimization problems. Unlike the standard cuckoo search which relies on Levy flight, the proposed method uses two types of flight that are chosen adaptively by a learning automaton to control the global and local search ability of the method during the run. Furthermore, a variable nest scheme and a new cuckoo addition mechanism are introduced. A greedy local search method is also integrated to refine the best found solution. An extensive set of experiments is conducted on a variety of dynamic environments generated by the moving peaks benchmark, to evaluate the performance of the proposed approach. Results are also compared with those of other state-of-the-art algorithms from the literature. The experimental results indicate the effectiveness of the proposed approach.