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IEEE World Congress on Computational Intelligence (WCCI2002)

作者： Mühlenbein, H Mahnig, T FhG-AIS Germany

ISBN: (纸本)0780372824

For discrete optimization, the two basic search principles prevailing are stochastic local search and population based search. Local search has difficulties to get out of local optima. Here variable neighborhood search outperforms stochastic local search methods which accept worse points with a certain probability. population based search performs best on problems with sharp gaps. It is outperformed by stochastic local search only when there are many paths to good local optima.

关键词： stochastic local search population based algorithms univariate marginal distribution algorithm UMDA

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A hybrid self-adaptive sine cosine algorithm with opposition based learning

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 119卷 210-230页

作者： Gupta, Shubham Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India

Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space of the problem to find the global optimal solution. Sine Cosine Algorithm (SCA) is a recently developed population-based meta-heuristic algorithm for solving global optimization problems. SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. But, like other population-based optimization algorithms, SCA also suffers the problem of low diversity, stagnation in local optima and skipping of true solutions. Therefore, in the present work, an attempt has been made towards the eradication of these issues, by proposing a modified version of SCA. The proposed algorithm is named as modified Sine Cosine Algorithm (m-SCA). In m-SCA, the opposite population is generated using opposite numbers based on perturbation rate to jump out from the local optima. Secondly, in the search equations of SCA self-adaptive component is added to exploit all the promising search regions which are pre-visited. To evaluate the effectiveness in solving the global optimization problems, m-SCA has been tested on two sets of benchmark problems - classical set of 23 well-known benchmark problems and standard IEEE CEC 2014 benchmark test problems. In the paper, the performance of proposed algorithm m-SCA is also tested on five engineering optimization problems. The conducted statistical, convergence and average distance analysis demonstrate the efficacy of the proposed algorithm to determine the efficient solution of real-life global optimization problems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： population based algorithms Sine Cosine algorithm (SCA) Opposition based learning Self-adaptation Benchmark test problems Engineering application problems

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Improved sine cosine algorithm with crossover scheme for global optimization

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KNOWLEDGE-based SYSTEMS 2019年 165卷 374-406页

作者： Gupta, Shubham Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India

Sine Cosine Algorithm is a recently developed algorithm based on the characteristics of sine and cosine trigonometric functions, to solve global optimization problems. This paper introduces a novel improved version of sine cosine algorithm, which enhances the exploitation ability of solutions and reduces the overflow of diversity present in the search equations of classical SCA. The proposed algorithm is named as [SCA, The key feature in the proposed algorithm is the hybridization of exploitation skills of crossover with personal best state of individual solutions and integration of self-learning and global search mechanisms. To evaluate these skills in ISCA, a classical set of well-known benchmark problems, standard IEEE CEC 2014 benchmark test and a recent set of benchmark problems, IEEE CEC 2017 have been taken. Several performance metrics (such as convergence, statistical test, performance index), employed on ISCA, ensure the robustness and efficiency of the algorithm. In the paper, the proposed algorithm ISCA is also used to solve five well-known engineering optimization problems. At the end of the paper, the proposed algorithm is also used for multilevel thresholding in image segmentation. The numerical experiments and analysis demonstrate that the proposed algorithm (ISCA) can be highly effective in solving real-life optimization problems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Optimization population based algorithms Sine cosine algorithm Engineering optimization problems Multilevel thresholding

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A multi-objective algorithm for optimization of modern machining processes

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 61卷 103-125页

作者： Rao, R. Venkata Rai, Dhiraj P. Balic, Joze SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India Univ Maribor Fac Mech Engn Maribor Slovenia

Multi-objective optimization aspects of four modern machining processes namely wire-electro discharge machining process, laser cutting process, electrochemical machining process and focused ion beam micro milling process are considered in this work. In WEDM process cutting velocity and surface quality are important objectives which are mutually conflicting in nature. Minimization of kerf taper is vital in the laser cutting process which increases with the increase in material removal rate. The ECM process is characterized by high material removal rate, but poor dimensional accuracy, high tool wear rate and high over cut. FIB micro-milling process is useful in applications where a nano-level surface finish is desired but this process is characterized by a very low material removal rate. All the above mentioned objectives are vital as they closely govern the performance of the machining processes considered in this work. Therefore, the aim of this work is to achieve these objectives through process parameter optimization. In order to handle multiple objectives simultaneously a new posteriori multi-objective optimization algorithm named as multi-objective Jaya (MO-Jaya) algorithm is proposed which can provide multiple optimal solutions in a single simulation run. The regression models for the above mentioned machining processes which were developed by previous researchers are used as fitness function for MO-Jaya algorithm. In the case of WEDM process the optimization problem is an unconstrained, linear and parameter bounded. In the case of laser cutting process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. In the ECM process the optimization problem is a non-linear, unconstrained, quadratic and parameter bounded. The second case study of ECM process the optimization problem is a non-linear, constrained, non-quadratic and parameter bounded. In the case of FIB micro-milling process, the optimization problem is a non-linear, unc

关键词： Modern machining processes Process parameters Multi-objective optimization A posteriori approach Jaya algorithm population based algorithms

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Advance Teaching-Learning based Optimization for Global Function Optimization 3rd

Advance Teaching-Learning Based Optimization for Global Func...

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3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI)

作者： Verma, Anand Agrawal, Shikha Agrawal, Jitendra Sharma, Sanjeev Rajiv Gandhi Proudyogiki Vishwavidyalaya Sch Informat Technol Bhopal India Rajiv Gandhi Proudyogiki Vishwavidyalaya Univ Inst Technol Bhopal India

ISBN: (纸本)9788132225386;9788132225379

Teaching-Learning based optimization (TLBO) is an evolutionary powerful algorithm in optimal solutions search space that is inspired from teaching learning phenomenon of a classroom. It is a novel population based algorithm with faster convergence speed and without any algorithm specific parameters. The present work proposes an improved version of TLBO called the Advance Teaching-Learning based Optimization (ATLBO). In this algorithm introduced a new weight parameter for more accuracy and faster convergence rate. The effectiveness of the method is compare against original TLBO on many benchmark problems with different characteristics and shows the improvement in performance of ATLBO over traditional TLBO.

关键词： Global function optimization Teaching-learning based optimization (TLBO) population based algorithms Convergence speed

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WHO: A New Evolutionary Algorithm Bio-Inspired by Wildebeests with a Case Study on Bank Customer Segmentation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第5期33卷

作者： Motevali, Mohammad Mandi Shanghooshabad, Ali Mohammadi Aram, Reza Zohouri Keshavarz, Hamidreza Islamic Azad Univ Comp Engn Dept Semnan Branch Semnan *** Iran Tarbiat Modares Univ Fac Elect & Comp Engn Al Ahmad St 7 Jalal Tehran Tehran Province Iran

Numerous evolutionary algorithms have been proposed which are inspired by the amazing lives of creatures, such as animals, insects, and birds. Each inspired algorithm has its own advantages and disadvantages, and has its own way to accomplish exploration and exploitation. In this paper, a new evolutionary algorithm with novel concepts, called Wildebeests Herd Optimization (WHO), is proposed. This algorithm is inspired by the splendid life of wildebeests in Africa. Moving and migration are inseparable from wildebeests' lives. When a wildebeest wants to choose its path during migration, it considers the best path known to itself, the location of the more mature wildebeests in the crowd, and the direction of wildebeests with high mobility. The WHO algorithm imitates these traits, and can concurrently explore and exploit the search space. For validating WHO, it is applied to optimization problems and data mining tasks. It is demonstrated that WHO outperforms other evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization, in the assessed problems. Then, WHO is applied to the customer segmentation problem. Customer segmentation is one of the most important tasks of data mining, especially in the banking sector. In this paper, the customers of a bank with current accounts are segmented using WHO based on four aspects: profitability, cost, loyalty and credit;some of these aspects are calculated in a novel way. The results were welcome by the bank authorities.

关键词： Wildebeests herd optimization (WHO) population based algorithms evolutionary clustering meta-heuristic clustering customer relationship management customer segmentation

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Metaheuristics: Some Principles for an Efficient Design

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Computer Technology and Application 2012年第6期3卷 446-462页

作者： Nicolas Zufferey Faculty of Economics and Social Sciences HEC-University of Geneva

Many optimization problems (from academia or industry) require the use of a metaheuristic to find a satisfying solution in a reasonable amount of time, even if optimality is not guaranteed. Metaheuristics can be roughly partitioned in two groups: local search methods (e. g., simulated annealing, tabu search, and variable neighborhood search) and population based algorithms (e. g., genetic algorithms, ant colonies, and scatter search). The reader is assumed to be familiar with the most popular metaheuristics. Even if there exist convergence theorems for some metaheuristics, they usually do not help to develop an efficient metaheuristic. The goal of this paper is to propose general rules which are useful when designing metaheuristics in order to produce good performance according to several criteria, independently of the class of metaheuristics employed. The discussion is illustrated for three well-known optimization problems: graph coloring, vehicle routing and job-shop scheduling.

关键词： Metaheuristics local search population based algorithms optimization

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