Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems

Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

Chaotic Local Search-Based Differential Evolution Algorithms for Optimization

Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things

To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.

Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

Improved gravitational search algorithm with crossover

Harmony search (HS) has shown promising performance in a wide range of real-world applications. However, in many cases, the basic HS exhibits strong exploration ability but weak exploitation capability. In order to enhance the exploitation capability of the basic HS, this paper presents an improved global harmony search with generalized opposition-based learning strategy (GOGHS). In GOGHS, the valuable information from the best harmony is utilized to enhance the exploitation capability. Moreover, the generalized opposition-based learning (GOBL) strategy is incorporated to increase the probability of finding the global optimum. The performance of GOGHS is evaluated on a set of benchmark test functions and is compared with several HS variants. The experimental results show that GOGHS can obtain competitive results.

Global harmony search with generalized opposition-based learning

Harmony search (HS) is a new evolutionary algorithm inspired by the process of music improvisation. During the past decade, HS has shown excellent performance in many fields. However, its search strategy often demonstrates insufficient exploitation ability when facing some complex practical problems. Moreover, the HS performance is significantly influenced by its control parameters. To enhance the search efficiency, an adaptive harmony search with best-based search strategy (ABHS) is proposed. In the search process, ABHS exploits the beneficial information from the global-best solution to improve the search ability, while it adaptively tunes its control parameters according to the feedback from the search process. Experiments are conducted on a set of classical test functions. The experimental results show that ABHS significantly enhances the search efficiency of HS.

Adaptive harmony search with best-based search strategy

Differential evolution (DE) is a simple yet efficient stochastic search approach for numerical optimization. However, it tends to suffer from slow convergence when tackling complicated problems. In addition, its search ability is significantly influenced by its control parameters. To improve the performance of the basic DE, this paper proposes a self-adaptive differential evolution with global neighborhood search (NSSDE). In the proposed NSSDE, its control parameters are self-adaptively tuned according to the feedback from the search process, while the global neighborhood search strategy is incorporated to accelerate the convergence speed. To evaluate the performance of the proposed NSSDE, we compare it with several DE variants on a set of benchmark test functions. The experimental results show that NSSDE can achieve better results than its competitors on the majority of the benchmark test functions.

Self-adaptive differential evolution with global neighborhood search

Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions.

/pdf/an-enhanced-differential-evolution-with-elite-chaotic-local-4o2m68h1tp.pdf

Zhaolu Guo

Papers

Improved gravitational search algorithm with crossover

Global harmony search with generalized opposition-based learning

Adaptive harmony search with best-based search strategy

Self-adaptive differential evolution with global neighborhood search

An enhanced differential evolution with elite chaotic local search