Opposition-based Population Initialization Research Articles

A Hybrid Greedy Sine Cosine Algorithm with Differential Evolution for Global Optimization and Cylindricity Error Evaluation

ABSTRACT Sine-cosine algorithm (SCA) has found a widespread application in various engineering optimization problems. However, SCA suffers from premature convergence and insufficient exploitation. Cylindricity error evaluation is a typical engineering optimization problem related to the quality of cylindrical parts. A hybrid greedy sine-cosine algorithm with differential evolution (HGSCADE) is developed in this paper to solve optimization problems and evaluate cylindricity error. HGSCADE integrates the SCA with the opposition-based population initialization, the greedy search, the differential evolution (DE), the success history-based parameter adaptation, and the Levy flight-based local search. HGSCADE is tested on the CEC2014 benchmark functions and is employed in cylindricity error evaluation. The results show the superiority of HGSCADE to other state-of-the-art algorithms for the benchmark functions and cylindricity error evaluation.

Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data

Grey wolf optimizer (GWO) is an efficient meta-heuristic algorithm that is inspired by the particular hunting behavior and leadership hierarchy of grey wolves in nature. In this paper, an efficient opposition-based grey wolf optimizer algorithm is proposed for solving the fuzzy clustering problem over artificial and real-life data. This work also tries to use the benefit of fuzzy properties which presents capability to handle overlapping clusters. However, centroid information and geometric structure information of clusters are the two important issues in fuzzy data clustering to improve the clustering performance. According to, in this paper, we derive two-objective functions, such as compactness and overlap–partition (OP) measures to handle above drawbacks. The centroid information issue is solved by compactness measure, and the OP measure is used to handle the geometric structure of clustering problem. Additionally, in the proposed clustering approach, the concept of opposition-based generation jumping and opposition-based population initialization is used with the standard GWO to enhance its computational speed and convergence profile. The efficiency of the proposed algorithm is shown for five artificial datasets and five real-life datasets of varying complexities. Experimental results show that the proposed method outperforms some existing methods with good clustering qualities.

Enhanced artificial bee colony algorithm through differential evolution

Artificial bee colony algorithm (ABC) is a relatively new optimization algorithm. However, ABC does well in exploration but badly in exploitation. One possible way to improve the exploitation ability of the algorithm is to combine ABC with other operations. Differential evolution (DE) can be considered as a good choice for this purpose. Based on this consideration, we propose a new algorithm, i.e. DGABC, which combines DE with gbest-guided ABC (GABC) by an evaluation strategy with an attempt to utilize more prior information of the previous search experience to speed up the convergence. In addition, to improve the global convergence, when producing the initial population, a chaotic opposition-based population initialization method is employed. The comparison results on a set of 27 benchmark functions demonstrate that the proposed method has better performance than the other algorithms.

Opposition-based learning in shuffled frog leaping: An application for parameter identification

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled frog leaping (SFL). The SFL divides a population into several memeplexes and then improves each memeplex in an evolutionary process. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the SFL which employ on one hand the OBL to accelerate the SFL without making premature convergence and on the other hand use the OBL strategy to diversify search moves of SFL. Four versions of SFL algorithm are proposed by incorporating the OBL and the SFL. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments are performed on parameter identification problems. The obtained results demonstrate that incorporating the opposition-based strategy and SFL performed in a proper way is a good idea to enhance performance of SFL. Two versions of opposition-based SFL outperform their pure competitor i.e., SFL in terms of all aspects on all problems but two other versions of SFL obtained a worse performance than the pure SFL. Also some performance comparisons of the proposed algorithms with some other algorithms reported in the literature confirm a significantly better performance of our proposed algorithms. Also in final part of the comparison study, a comparison of the proposed algorithm in this study in respect to other algorithms on CEC05 functions demonstrates a completely comparable performance of proposed algorithm.

Multiobjective Optimal Reactive Power Dispatch and Voltage Control: A New Opposition-Based Self-Adaptive Modified Gravitational Search Algorithm

This paper presents a novel opposition-based self-adaptive modified gravitational search algorithm (OSAMGSA) for optimal reactive power dispatch and voltage control in power-system operation. The problem is formulated as a mixed integer, nonlinear optimization problem, which has both continuous and discrete control variables. In order to achieve the optimal value of loss, voltage deviation, and voltage stability index, it is necessary to find the optimal value of control variables such as the tap positions of tap changing transformers, generator voltages, and compensation capacitor. Therefore, this complicated problem needs to be solved by an accurate optimization algorithm. This paper solves the aforementioned problem by using the gravitational search algorithm (GSA), which is one of the novel optimization algorithms based on the gravity law and mass interactions. To improve the efficiency of this algorithm, the tuning of its parameters is accomplished using random generation, and by applying the self-adaptive parameter tuning scheme. Also, the proposed OSAMGSA of this paper employs the opposition-based population initialization and self-adaptive probabilistic learning approach for generation jumping and escaping from local optima. Since the proposed problem is a multiobjective optimization problem incorporating several solutions instead of one, we applied the Pareto optimal solution method in order to find all Pareto optimal solutions. Moreover, the fuzzy decision method is used for obtaining the best compromise solution between them.

OGDE3: Opposition-Based Third Generalized Differential Evolution

Multi-Objective Optimization (MOO) metaheuristics are commonly used for solving complex MOO problems characterized by non-convexity, multimodality, mixed-types variables, non-linearity, and other complexities. However, often metaheuristics suffer from slow convergence. Opposition-Based Learning (OBL) has been successfully used in the past for acceleration of single-objective metaheuristics. The most successful example in this regard is Opposition-based Differential Evolution (ODE). However, OBL was not fully explored for MOO metaheuristics. Therefore, in this paper, to the best of our knowledge, for the first time OBL is successfully adapted for a MOO metaheuristic by using a single population (no coevolution). The proposed MOO metaheuristic is based on the GDE3 method and it is called Opposition-based GDE3 (OGDE3). OGDE3 utilizes OBL for opposition-based population initialization and self-adaptive oppositionbased generating jumping. Furthermore, the new algorithm is compared with seven state-of-the-artMOO metaheuristics using the ZDT test suite. OGDE3 outperformed the other algorithms; the results are explained and discussed in detail.

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.

Opposition-based learning in the shuffled differential evolution algorithm

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differential evolution (SDE). In the SDE, population is divided into several memeplexes and each memeplex is improved by the differential evolution (DE) algorithm. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the DE which, on one hand, use the partitioning and shuffling concepts of SDE to compensate for the limited amount of search moves of the original DE and, on the other hand, employ the OBL to accelerate the DE without making premature convergence. Four versions of DE algorithm are proposed based on the OBL and SDE strategies. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments on 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005 and non-parametric analysis of obtained results demonstrate that the performances of the proposed algorithms are better than the SDE. The fourth version of proposed algorithm has a significant difference compared to the SDE in terms of all considered aspects. The emphasis of comparison results is to obtain some successful performances on unsolved functions for the first time, which so far have not been reported any successful runs on them. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE algorithms reported in the literature confirm a significantly better performance of our proposed algorithm, especially on high-dimensional functions.

Enhancing particle swarm optimization using generalized opposition-based learning

Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems.

Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

This paper presents a novel algorithm based on generalized opposition-based learning (GOBL) to improve the performance of differential evolution (DE) to solve high-dimensional optimization problems efficiently. The proposed approach, namely GODE, employs similar schemes of opposition-based DE (ODE) for opposition-based population initialization and generation jumping with GOBL. Experiments are conducted to verify the performance of GODE on 19 high-dimensional problems with D = 50, 100, 200, 500, 1,000. The results confirm that GODE outperforms classical DE, real-coded CHC (crossgenerational elitist selection, heterogeneous recombination, and cataclysmic mutation) and G-CMA-ES (restart covariant matrix evolutionary strategy) on the majority of test problems.

Solving large scale optimization problems by opposition-based differential evolution (ODE)

This work investigates the performance of Differential Evolution (DE) and its opposition-based version (ODE) on large scale optimization problems. Opposition-based differential evolution (ODE) has been proposed based on DE; it employs opposition-based population initialization and generation jumping to accelerate convergence speed. ODE shows promising results in terms of convergence rate, robustness, and solution accuracy. A recently proposed seven-function benchmark test suite for the CEC-2008 special session and competition on large scale global optimization has been utilized for the current investigation. Results interestingly confirm that ODE outperforms its parent algorithm (DE) on all high dimensional (500D and 1000D) benchmark functions (F1-F7). Furthermore, authors recommend to utilize ODE for more complex search spaces as well. Because results confirm that ODE performs much better than DE when the dimensionality of the problems is increased from 500D to 1000D. All required details about the testing platform, comparison methodology, and also achieved results are provided.

A novel population initialization method for accelerating evolutionary algorithms

Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then random initialization is the most commonly used method to generate candidate solutions (initial population). This paper proposes a novel initialization approach which employs opposition-based learning to generate initial population. The conducted experiments over a comprehensive set of benchmark functions demonstrate that replacing the random initialization with the opposition-based population initialization can accelerate convergence speed.