New facet of honey bees dancing language for mining the induction rules

Artificial Bee Colony (ABC) algorithm is used in many domains of computation, including optimization, clustering and classification tasks. Further, honey bees dancing is one of the most fascinating and intriguing behaviours of animal life. Honey bees’ dancing is termed as “waggle Dance” in literature and they perform it for indicating the food sources in their environment. This work presents a novel honey bees dancing language (HBDL)-based algorithm for mining the induction rules from datasets. The proposed HBDL algorithm is implemented and tested against the performance of ABC, Particle Swarm Optimization and nine more traditional algorithms frequently used by researchers. The experimental results showed that HBDL is a suitable and effective technique for data mining and classification task.     

An artificial bee colony algorithm for the economic lot scheduling problem

In this study, we present an artificial bee colony (ABC) algorithm for the economic lot scheduling problem modelled through the extended basic period (EBP) approach. We allow both power-of-two (PoT) and non-power-of-two multipliers in the solution representation. We develop mutation strategies to generate neighbouring food sources for the ABC algorithm and these strategies are also used to develop two different variable neighbourhood search algorithms to further enhance the solution quality. Our algorithm maintains both feasible and infeasible solutions in the population through the use of some sophisticated constraint handling methods. Experimental results show that the proposed algorithm succeeds to find the all the best-known EBP solutions for the high utilisation 10-item benchmark problems and improves the best known solutions for two of the six low utilisation 10-item benchmark problems. In addition, we develop a new problem instance with 50 items and run it at different utilisation levels ranging from 50 to 99% to see the effectiveness of the proposed algorithm on large instances. We show that the proposed ABC algorithm with mixed solution representation outperforms the ABC that is restricted only to PoT multipliers at almost all utilisation levels of the large instance.     

An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times

This study deals with a sequence-dependent disassembly line balancing problem by considering the interactions among disassembly tasks, and a multi-objective mathematical model is established. Subsequently, a novel hybrid artificial bee colony algorithm is proposed to solve the problem. A new rule is used to initialize a bee colony population with certain diversity, and a dynamic neighbourhood search method is introduced to guide the employed/onlooker bees to promising regions. To rapidly leave the local optima, a global learning strategy is employed to explore higher quality solutions. In addition, a multi-stage evaluation method is designed for onlookers to effectively select employed bees to follow. The performance of the proposed algorithm is tested on a set of benchmark instances and two case scenarios, and the results are compared with several other metaheuristics in terms of solution quality and computation time. The comparisons demonstrate that the proposed algorithm exhibits superior performance.     

Elephant swarm water search algorithm for global optimization

The rising complexity of real-life optimization problems has constantly inspired computer researchers to develop new efficient optimization methods. Evolutionary computation and metaheuristics based on swarm intelligence are very popular nature-inspired optimization techniques. In this paper, the author has proposed a novel elephant swarm water search algorithm (ESWSA) inspired by the behaviour of social elephants, to solve different optimization problems. This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought. Initially, we perform preliminary parametric sensitivity analysis for our proposed algorithm, developing guidelines for choosing the parameter values in real-life problems. In addition, the algorithm is evaluated against a number of widely used benchmark functions for global optimizations, and it is observed that the proposed algorithm has better performance for most of the cases compared with other state-of-the-art metaheuristics. Moreover, ESWSA performs better during fitness test, convergence test, computational complexity test, success rate test and scalability test for most of the benchmarks. Next, ESWSA is tested against two well-known constrained optimization problems, where ESWSA is found to be very efficient in term of execution speed and best fitness. As an application of ESWSA to real-life problem, it has been tested against a benchmark problem of computational biology, i.e., inference of Gene Regulatory Network based on Recurrent Neural Network. It has been observed that the proposed ESWSA is able to reach nearest to global minima and enabled inference of all true regulations of GRN correctly with less computational time compared with the other existing metaheuristics.     

求解约束优化问题的改进布谷鸟搜索算法

为了提高约束优化问题的求解精度和收敛速度,提出求解约束优化问题的改进布谷鸟搜索算法。首先分析了基本布谷鸟搜索算法全局搜索和局部搜索过程中的不足,对其中全局搜索和局部搜索迭代公式进行重新定义,然后以一定概率在最优解附近进行搜索。对12个标准约束优化问题和4个工程约束优化问题进行测试并与多种算法进行对比,实验结果和统计分析表明所提算法在求解约束优化问题上具有较强的优越性。     

Guided particle swarm optimization method to solve general nonlinear optimization problems

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder–Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.     

基于混沌分组教与学优化算法锅炉NOx模型优化研究

为了平衡教与学优化算法的全局和局部搜索能力,提出一种混沌分组教与学优化算法。采用3种调整机制:应用混沌方法初始化种群个体;在教阶段成绩更新中引入自适应惯性权值;在学阶段,采用随机蛙跳算法思想,将班级中的学生分组,更新子种群的最差解。用10个经典的测试集函数测试改进算法的性能,并与人工蜂群算法、万有引力算法、原始的教学优化算法进行比较,结果显示:改进算法具有良好的全局和局部搜索能力,而且收敛精度高。此外,应用改进的教与学算法优化循环流化床锅炉氮氧化合物排放浓度的模型,仿真试验表明优化后的模型具有良好的辨识能力和泛化能力,能够指导工程,解决实际问题。     

An evolutionary algorithm for global optimization based on self-organizing maps

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.     

All-terminal network reliability optimization via probabilistic solution discovery

This paper presents a new algorithm that can be readily applied to solve the all-terminal network reliability allocation problems. The optimization problem solved considers the minimization of the network design cost subject to a known constraint on all-terminal reliability by assuming that the network contains a known number of functionally equivalent components (with different performance specifications) that can be used to provide redundancy. The algorithm is based on two major steps that use a probabilistic solution discovery approach and Monte Carlo simulation to generate the quasi-optimal network designs. Examples for different sizes of all-terminal networks are used throughout the paper to illustrate the approach. The results obtained for the larger networks with unknown optima show that the quality of the solutions generated by the proposed algorithm is significantly higher with respect to other approaches and that these solutions are obtained from restricted solution search space. Although developed for all-terminal reliability optimization, the algorithm can be easily applied in other resource-constrained allocation problems.     

A novel hybrid combination optimization algorithm based on search area segmentation and fast Fourier transform

A novel hybrid optimization algorithm combining search area segmentation technique and the fast Fourier transform (HSAS/FFT) is presented to solve the numerical optimization problems. Firstly, the spectrum of each dimension of the objective function can be acquired by the FFT. The search space is segmented by using the spectrum to ensure that each subspace is unimodal. Secondly, the population of subspaces is produced and the optimal individual can be obtained by gradient descent algorithm. Finally, the local optimal solution in the optimal subspace is generated by the binary search algorithm. Make the optimal individual the new search space and repeat the process until meeting the termination condition. The proposed HSAS/FFT was tested on the CEC2017 benchmark, which evaluates the performance of the proposed algorithm on solving global optimization problems. Results obtained show that HSAS/FFT has an excellent performance and better convergence speed in comparison with some of the state-of-the-art algorithms.     

An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system

This paper proposes an improved artificial bee colony (IABC) algorithm for addressing the distributed flow shop considering the distance coefficient found in precast concrete production system, with the minimisation of the makespan. In the proposed algorithm, each solution is first represented by a two-dimensional vector, where the first dimensional vector is the factory and the second dimensional vector lists the operation scheduling sequence of each factory. Second, considering the distributed problem feature, a distributed iterated greedy heuristic (DIG) is developed where destruction and construction processes are designed in detail while considering the distributed structures. Third, an efficient population initialisation method that considers the factory workload balance is presented. Then, a local search approach that randomly replaces two factories with two randomly selected jobs and that finds an optimal position for the two inserted operations via the DIG method is proposed. For the canonical ABC algorithm, using the DIG approach, the main three parts are improved, namely, the employee, onlooker, and scout bees. Finally, the proposed algorithm is tested on sets of extended instances based on the well-known benchmarks. Through an analysis of the experimental results, the highly effective proposed IABC algorithm is compared to several efficient algorithms drawn from the literature.     

A TABOO SEARCH BASED APPROACH TO FIND THE PARETO OPTIMAL SET IN MULTIPLE OBJECTIVE OPTIMIZATION

Taboo search is a heuristic optimization technique which works with a neighbourhood of solutions to optimize a given objective function. It is generally applied to single objective optimization problems. Taboo search has the potential for solving multiple objective optimization (MOO) problems, because it works with more than one solution at a time, and this gives it the opportunity to evaluate multiple objective functions simultaneously. In this paper, a taboo search based algorithm is developed to find Pareto optimal solutions in multiple objective optimization problems. The developed algorithm has been tested with a number of problems and compared with other techniques. Results obtained from this work have proved that a taboo search based algorithm can find Pareto optimal solutions in MOO effectively.     

An implementation of the iterated tabu search algorithm for the quadratic assignment problem

In this paper, we describe an implementation of the iterated tabu search (ITS) algorithm for the quadratic assignment problem (QAP), which is one of the well-known problems in combinatorial optimization. The medium- and large-scale QAPs are not, to this date, practically solvable to optimality, therefore heuristic algorithms are widely used. In the proposed ITS approach, intensification and diversification mechanisms are combined in a proper way. The goal of intensification is to search for good solutions in the neighbourhood of a given solution, while diversification is responsible for escaping from local optima and moving towards new regions of the search space. In particular, the following enhancements were implemented: new formula for fast evaluation of the objective function and efficient data structure; extended intensification mechanisms (including randomized tabu criterion, combination of tabu search and local search, dynamic tabu list maintaining); enhanced diversification strategy using periodic tabu tenure and special mutation procedure. The ITS algorithm is tested on the different instances taken from the QAP library QAPLIB. The results from the experiments demonstrate promising efficiency of the proposed algorithm, especially for the random QAP instances.     

Hybridization of ABC and PSO algorithms for improved solutions of RCPSP

A resource-constrained project scheduling problem (RCPSP) is one of the most famous intractable NP-hard problems in the operational research area in terms of its practical value and research significance. To effectively solve the RCPSP, we propose a hybrid approach by integrating artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Moreover, a novel structure of ABC-PSO is devised based on embedded ABC-PSO (EABC-PSO) and sequential ABC-PSO (SABC-PSO) strategies. The EABC-PSO strategy mainly applies the PSO algorithm to update the process of the ABC algorithm while the SABC-PSO strategy demonstrates an approach in which computational results obtained from the ABC algorithm are further improved based on the PSO algorithm. In both strategies, bees in the ABC process are entitled to learning capacity from the best local and global solutions in terms of the PSO concept. Subsequently, the updates of solutions are premeditated with crossover and insert operators together with double justification methods. Computational results obtained from the tests on benchmark sets show that the proposed ABC-PSO algorithm is efficient in solving RCPSP problems, demonstrating clear advantages over the pure ABC algorithm, the PSO algorithm, and a number of listed heuristics.     

Hybridizing the harmony search algorithm with a spreadsheet ‘Solver’ for solving continuous engineering optimization problems

In this article, a hybrid global–local optimization algorithm is proposed to solve continuous engineering optimization problems. In the proposed algorithm, the harmony search (HS) algorithm is used as a global-search method and hybridized with a spreadsheet ‘Solver’ to improve the results of the HS algorithm. With this purpose, the hybrid HS–Solver algorithm has been proposed. In order to test the performance of the proposed hybrid HS–Solver algorithm, several unconstrained, constrained, and structural-engineering optimization problems have been solved and their results are compared with other deterministic and stochastic solution methods. Also, an empirical study has been carried out to test the performance of the proposed hybrid HS–Solver algorithm for different sets of HS solution parameters. Identified results showed that the hybrid HS–Solver algorithm requires fewer iterations and gives more effective results than other deterministic and stochastic solution algorithms.     

SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.     

Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.     

求解约束优化问题的退火遗传算法

针对基于罚函数遗传算法求解实际约束优化问题的困难与缺点,提出了求解约束优化问题的退火遗传算法。对种群中的个体定义了不可行度,并设计退火遗传选择操作。算法分三阶段进行,首先用退火算法搜索产生初始种群体,随后利用遗传算法使搜索逐渐收敛于可行的全局最优解或较优解,最后用退火优化算法对解进行局部优化。两个典型的仿真例子计算结果证明该算法能极大地提高计算稳定性和精度。     

Improved artificial bee colony algorithm based on escaped foraging strategy

This paper presents an improved artificial bee colony algorithm. Under the framework of the basic artificial bee colony algorithm, this paper redefines the artificial bee colony and introduces search strategies for group escape and foraging based on Levy flight. The proposed algorithm is named artificial bee colony algorithm based on escaped foraging strategy (EFSABC).There are different strategies for scout bees, onlookers, and free bees searching for honey sources in the EFSABC: all working bees relinquish old honey sources due to disturbance, and select different routines to seek new honey sources. Sixteen typical high-dimensional standard functions are used to verify the effectiveness of the proposed algorithm. The EFSABC algorithm outperforms the traditional artificial bee colony algorithm in all aspects.     

An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.