Opposition-based Learning Method Research Articles

A discrete oppositional multi-verse optimization algorithm for multi-skill resource constrained project scheduling problem

In this paper, a discrete oppositional multi-verse optimization (DOMVO) algorithm is proposed to address multi-skill resource constrained project scheduling problem (MS-RCPSP). Firstly, the black/white holes phase in DOMVO algorithm is designed by integrating path relinking technique. Secondly, two improved path relinking methods are presented and embedded into the proposed scheme to enhance search abilities. Thirdly, the opposition-based learning (OBL) method is employed as a hybrid strategy to improve the quality of solutions. Moreover, a repair-based decoding scheme is developed to generate schedules more efficiently. Additionally, the design-of-experiment (DOE) method is carried out to investigate the influence of parameters setting. Finally, the effectiveness of DOMVO is evaluated on the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset and the computational comparisons indicate the superiority of the proposed DOMVO over the state-of-the-art algorithms in solving MS-RCPSP.

On the improvement in grey wolf optimization

Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative abilities of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications: Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition-based learning method has been used in the initial half of iterations to provide diversity among the search agents. The proposed approach has been tested on standard benchmarking functions for different population and dimension sizes to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than GWO, bat algorithm, bat flower pollinator, chicken swarm optimization, differential evolution, firefly algorithm, flower pollination algorithm (FPA) and grasshopper optimization algorithm. Statistical testing of GWO-E has been done to prove its significance over other popular algorithms. Further, as a real-world application, the GWO-E is used to design non-uniform linear antenna array (LAA) for minimum possible sidelobe level and null control. Performance of GWO-E for the synthesis of LAA is evaluated by considering the several different case studies of LAA that exists in the literature, and the results are compared with the results of other popular meta-heuristic algorithms like genetic algorithm, ant lion algorithm, FPA, cat swarm optimization, GWO and many more. Numerical results further show the superior performance of GWO-E over original GWO and other popular algorithms.

An enhanced hybridized artificial bee colony algorithm for optimization problems

Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

Multi-area economic dispatch using an improved stochastic fractal search algorithm

Multi-area economic dispatch (MAED) characterized by high non-convexity and non-linearity is an important issue in power system operation. This paper presents an improved stochastic fractal search (ISFS) to solve the MAED problem considering the area load demands, the tie-line limits and various operating constraints. To balance exploration and exploitation, the ISFS introduces an opposition-based learning method for population initialization as well as for generation jumping. By combining with the differential evolution strategy, a hybrid diffusion process is developed and used as the local search technique to enhance the exploitation ability. Furthermore, a novel repair-based penalty approach is presented and incorporated into the ISFS to find feasible solutions more efficiently. The effectiveness and robustness of the ISFS is evaluated on several test systems consisting of 16–120 generating units. Computational results demonstrate the superiority of the proposed ISFS scheme over the state-of-the-art algorithms.

EMCSO: An Elitist Multi-Objective Cat Swarm Optimization

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses thenon-dominated sorting method to findthe solutionsas close as to POFand crowding distance technique toobtain a uniform distribution among thenon-dominated solutions. Also, the algorithm is allowedto keep the elites of population in reproduction processand use an opposition-based learning method for population initialization to enhance the convergence speed.The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based onperformance measures of generational distance (GD), inverted GD, spread,and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve multi-objective knapsack problem.

Adaptive Beamforming Algorithm Based on a Simulated Kalman Filter

Adaptive beamforming is a technique used to steer the radiation pattern towards the desired signal and cancel out any interference signal by finding the appropriate weights for every element in an array antenna, to achieve maximum signal to interference plus noise ratio (SINR). There are many methods to perform adaptive beamforming and one of the method is to use metaheuristic algorithm, to estimate the weights for individual elements in an array. Over the years, various metaheuristic algorithms have been applied to adaptive beamforming. Some of the metaheuristic algorithms have been modified from the original algorithms to improve the algorithms performance in adaptive beamforming application. A new metaheuristic algorithm named Simulated Kalman Filter (SKF), is inspired by the estimation capabilities of Kalman filter, has not been applied to adaptive beamforming application. Therefore, this research presents the first-time application of SKF algorithm to adaptive beamforming. The SKF algorithm, however, often converge prematurely at local optimum due to lack of exploration, preventing it from finding better solution. A modified version of the SKF algorithm, named Opposition-Based SKF (OBSKF), introduced by K. Zakwan, applies Opposition-Based Learning method to improve the exploration capabilities of SKF algorithm. Moreover, a new modified version of the SKF algorithm named SKF with Modified Measurement (SKFMM) is introduced to further improve the exploration capabilities of SKF algorithm by modifying the measurement-update equation. The SKF, OBSKF and SKFMM is applied to an array antenna with 10 elements arranged linearly with 0.5

A grey artificial bee colony algorithm

Artificial bee colony (ABC) algorithm is a very popular population-based algorithm. Unfortunately, there exists a shortcoming of slow convergence rate, which partly results from random choices of neighbor individuals regarding its solution search equation. A novel scheme for the choice of neighbors is introduced based on grey relational degrees between a current individual and its neighbors to overcome the insufficiency. Then, the chosen neighbor is used to guide the search process. Additionally, inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed. What is more, solution search equations using information of the best individual, an opposition-based learning method and a chaotic initialization technique are also integrated into the proposed algorithm called grey artificial bee colony algorithm (GABC for short). Subsequently, the effectiveness and efficiency of GABC are validated on a test suite composed of fifty-seven benchmark functions. Furthermore, it is also compared with a few state-of-the-art algorithms. The related experimental results show the effectiveness and superiority of GABC.

A novel artificial bee colony optimization strategy-based extreme learning machine algorithm

Extreme learning machine (ELM) is a kind of single-hidden layer feedforward neural networks (SLFNs). Compared with traditional neural networks algorithms, ELM is simpler in structure with higher learning speed and better generalization performance. Due to generating randomly input weights and biases of ELM, there can exist some non-optimal or unnecessary input weights and biases. In addition, ELM can need more hidden nodes which can make ELM respond slowly to unknown testing data. Consequently, a new NABC-ELM algorithm, which is optimized by a novel artificial bee colony called NABC, is proposed. To improve generalization performance of ELM, the NABC is applied to optimize input weights and biases. In NABC, the Tent chaotic opposition-based learning method is applied to initialize the population. Meanwhile, the self-adaptive search strategy is presented in the employed bee and onlooker bee phase. In addition, the Tent chaotic local search for scout bee is implemented. Finally, experiments on some popular classification data sets demonstrate that the proposed NABC-ELM can consistently get better generalization performance than some existing ELM variants.

An oppositional wolf pack algorithm for Parameter identification of the chaotic systems

Parameter identification is an important issue for chaotic systems, which have been investigated intensely in several fields such as secure communication, power converters and biological systems. To solve the above problem, most existing evolutionary algorithms were proposed. However, the existing evolutionary algorithms have their own limitations when solving the parameter identification problem. Therefore, we propose a novel and efficient oppositional wolf pack algorithm (OWPA), which has good balance of exploitation and exploration, to estimate the parameters of Lorenz chaotic system. First, an oppositional initial population producing method is proposed to enhance the global convergence. Second, the opposition-based learning method is presented for new wolf producing in wolf pack algorithm, which cannot only enhance the local searching ability of wolf pack algorithm in normal situation but also increase the diversity of distributed population in catastrophic situation. Furthermore, the proposed oppositional wolf pack algorithm is applied to estimate the parameters for Lorenz chaotic system under the offline and online condition. Finally, numerical example is provided to illustrate the effectiveness of the proposed algorithm. It is shown that the above proposed algorithm has more effectiveness and robustness than the existing popular algorithms (i.e., particle swarm optimization algorithm, firefly algorithm and cuckoo search algorithm).

Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering

Due to the ever increasing number of documents in the digital form, automated text clustering has become a promising method for the text analysis in last few decades. A major issue in the text clustering is high dimensionality of the feature space. Most of these features are irrelevant, redundant, and noisy that mislead the underlying algorithm. Therefore, feature selection is an essential step in the text clustering to reduce dimensionality of the feature space and to improve accuracy of the underlying clustering algorithm. In this paper, a hybrid intelligent algorithm, which combines the binary particle swarm optimization (BPSO) with opposition-based learning, chaotic map, fitness based dynamic inertia weight, and mutation, is proposed to solve feature selection problem in the text clustering. Here, fitness based dynamic inertia weight is integrated with the BPSO to control movement of the particles based on their current status, and the mutation and the chaotic strategy are applied to enhance the global search capability of the algorithm. Moreover, an opposition-based initialization is used to start with a set of promising and well-diversified solutions to achieve a better final solution. In addition, the opposition-based learning method is also used to generate opposite position of the gbest particle to get rid of the stagnation in the swarm. To prove effectiveness of the proposed method, experimental analysis is conducted on three different benchmark text datasets Reuters-21578, Classic4, and WebKB. The experimental results demonstrate that the proposed method selects more informative features set compared to the competitive methods as it attains higher clustering accuracy. Moreover, it also improves convergence speed of the BPSO.

A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

The permutation flow shop scheduling problem (PFSP) which is known to be NP-hard has been widely investigated in recent years. In this paper, an effective hybrid discrete biogeography-based optimization (HDBBO) algorithm is proposed for solving the PFSP with the objective to minimise the makespan. Opposition-based learning method and the NEH heuristic are utilised in the HDBBO to generate an initial population with certain quality and diversity. Moreover, a novel variable local search strategy is presented and incorporated within the biogeography-based optimization framework to improve the exploitation ability. Computational results on two typical benchmark suits and comparisons with some state-of-the-art algorithms are presented to show the effectiveness of the HDBBO scheme.

Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization

The artificial bee colony (ABC) algorithm is a simple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing exploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability Ps. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self-adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to enhance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition-based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark functions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.

An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning

Artificial bee colony ABC algorithm, which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization algorithm. It shows more effective than genetic algorithm GA, particle swarm optimization PSO and differential evolution DE. However, ABC algorithm can sometimes be slow to converge, and it is good at exploration but poor at exploitation regarding its solution search equation. To address these concerning issues, we propose a novel search strategy at the employed bees stage by introducing generalized opposition-based learning method as a search mechanism and an improved solution search equation by taking advantages of the local best solution at the onlookers stage. Both operations can balance the exploration and the exploitation for the proposed algorithm. Then, in order to enhance the global convergence, we modify dynamically frequency of perturbation at each iteration. In addition, we use a more robust calculation to determine and compare the quality of alternative solutions. Experiments are conducted on a set of 21 benchmark functions. The experimental results show that the proposed algorithm can outperform ABC-based algorithms and other significant evolutionary optimizers in solving complex numerical optimization problems.

Oppositional backtracking search optimization algorithm for parameter identification of hyperchaotic systems

Parameter identification is an important issue in nonlinear science and has received increasing interest in the recent years. In this paper, an oppositional backtracking search optimization algorithm is proposed to solve the parameter identification of hyperchaotic system. The backtracking search optimization algorithm provides a new alternative for population-based heuristic search. To increase the diversity of initial population and to accelerate the convergence speed, the opposition-based learning method is employed in the backtracking search optimization algorithm for population initialization as well as for generation jumping. Numerical simulations on several typical hyperchaotic systems are conducted to demonstrate the effectiveness and robustness of the proposed scheme.

Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions

In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur’s, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee’s search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.

A method for axis straightness error evaluation based on improved artificial bee colony algorithm

An improved artificial bee colony (IABC) optimization algorithm for the accurate evaluation of minimum zone axis straightness error from a set of coordinate measurement data points was proposed. In the proposed algorithm, the opposition-based learning method was employed to produce initial population and scouts, the employed bees used greedy selection mechanism to update the best food source achieved so far one by one, and a new search mechanism inspired by differential evaluation was used for onlookers. The nonlinear mathematical model for axis straightness error evaluation and the fitness function of IABC were introduced in detail. Four classical test functions were selected in the experiments; the simulation results verified the feasibility of IABC algorithm. According to two practical examples, the results obtained by the IABC algorithm are more accurate and efficient than other conventional methods. It is a unified approach for other form and position error evaluations and is well suited for high-precision measuring equipments such as the CMM.

Parameter estimation of chaotic systems by an oppositional seeker optimization algorithm

The parameter estimation can be formulated as a multi-dimensional optimization problem. By combining the seeker optimization algorithm with the opposition-based learning method, an oppositional seeker optimization algorithm is proposed in this work, and is applied to the parameter estimation of chaotic systems. The seeker optimization algorithm provides a new alternative for population-based heuristic search. By considering an estimate and its opposite of current solutions at the same time, the opposition-based learning method is employed for population initialization and also for generation jumping in seeker optimization algorithm. Numerical simulations on two typical chaotic systems are conducted to show the effectiveness and robustness of the proposed scheme.

Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System

Motion control of gun barrels is an ongoing topic for the development of gun control equipments possessing excellent performances. In this paper, a typical fractional order PID control strategy is employed for the gun control system. To obtain optimal parameters of the controller, a multiobjective optimization scheme is developed from the loop-shaping perspective. To solve the specified nonlinear optimization problem, a novel Pareto optimal solution based multiobjective differential evolution algorithm is proposed. To enhance the convergent rate of the optimization process, an opposition based learning method is embedded in the chaotic population initialization process. To enhance the robustness of the algorithm for different problems, an adapting scheme of the mutation operation is further employed. With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. The numerical simulation results show that the control system can rapidly follow the demand signal with high accuracy and high robustness, demonstrating the efficiency of the proposed controller parameter tuning method.

Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique

Particle swarm optimization (PSO) is a relatively new optimization algorithm that has been applied to a variety of problems. However, it may easily get trapped in a local optima when solving complex multimodal problems. To address this concerning issue, we propose a novel PSO called as CSPSO to improve the performance of PSO on complex multimodal problems in the paper. Specifically, a stochastic search technique is used to execute the exploration in PSO, so as to help the algorithm to jump out of the likely local optima. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. Moreover, numerical simulation and comparisons with some typical existing algorithms demonstrate the superiority of the proposed algorithm.