This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

/pdf/a-comprehensive-review-on-nsga-ii-for-multi-objective-4ihlcswg3c.pdf

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

One of the major problems in Big Data is a large number of features or dimensions, which causes the issue of “the curse of dimensionality” when applying machine learning, especially classification algorithms. Feature selection is an important technique which selects small and informative feature subsets to improve the learning performance. Feature selection is not an easy task due to its large and complex search space. Recently, swarm intelligence techniques have gained much attention from the feature selection community because of their simplicity and potential global search ability. However, there has been no comprehensive surveys on swarm intelligence for feature selection in classification which is the most widely investigated area in feature selection. Only a few short surveys is this area are still lack of in-depth discussions on the state-of-the-art methods, and the strengths and limitations of existing methods, particularly in terms of the representation and search mechanisms, which are two key components in adapting swarm intelligence to address feature selection problems. This paper presents a comprehensive survey on the state-of-the-art works applying swarm intelligence to achieve feature selection in classification, with a focus on the representation and search mechanisms. The expectation is to present an overview of different kinds of state-of-the-art approaches together with their advantages and disadvantages, encourage researchers to investigate more advanced methods, provide practitioners guidances for choosing the appropriate methods to be used in real-world scenarios, and discuss potential limitations and issues for future research.

A survey on swarm intelligence approaches to feature selection in data mining

Facing increasingly serious ecological problems, sustainable development and green manufacturing have attracted much attention. Meanwhile, with the development of globalization, distributed manufacturing is becoming widespread. This paper addresses an energy-efficient scheduling of the distributed permutation flow-shop (EEDPFSP) with the criteria of minimizing both makespan and total energy consumption. Considering the distributed and multiobjective optimization complexity, a knowledge-based cooperative algorithm (KCA) is proposed to solve the EEDPFSP. First, a cooperative initialization scheme is presented with both extended energy-efficient Nawaz–Enscore–Ham heuristic and slowest allowable speed rule that are specially designed to produce good initial solutions with certain diversity. Second, several properties of the nondominated solutions are investigated based on the characteristics of the bi-objective problem, which are used to develop the knowledge-based search operators. Third, a cooperative search strategy of multiple operators is designed for the solutions with different characteristics to tradeoff two objectives. Fourth, a knowledge-based local intensification is used for exploiting better nondominated solutions sufficiently. Moreover, an energy saving method based on the critical path is used to further improve the performance. The effect of parameter setting on the KCA is investigated with the Taguchi method of design-of-experiment. Extensive computational tests and comparisons are carried out, which verify the effectiveness of the special designs of the KCA in solving the EEDPFSP.

A Knowledge-Based Cooperative Algorithm for Energy-Efficient Scheduling of Distributed Flow-Shop

Optimizing Weighted Extreme Learning Machines for Imbalanced Classification and Application to Credit Card Fraud Detection

Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times

The multiobjective permutation flow shop scheduling problem with sequence dependent setup times has been an object of investigations for decades. This widely studied problem from the scheduling theory links the sophisticated solution algorithms with the moderate real world applications. This paper presents a novel multiobjective discrete artificial bee colony algorithm based decomposition, called MODABC/D , to solve the sequence dependent setup times multiobjective permutation flowshop scheduling problem with the objective to minimize makespan and total flowtime. First, in order to make the standard artificial bee colony algorithm to solve the scheduling problem, a discrete artificial bee colony algorithm is proposed to solve the problem based on the perturbation operation. Then, a problem-specific solution builder heuristic is used to initialize the population to enhance the quality of the initial solution. Finally, a further local search method are comprised of a single local search procedures based on the insertion neighborhood structures to find the better solution for the nonimproved individual. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the multiobjective discrete artificial bee colony algorithm-based decomposition is compared against the state of art algorithms from the existing literature in terms of both coverage value and hypervolume indicator.

Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times

This paper focuses on the multiobjective solutions of the flow shop scheduling problem with and without sequence dependent setup times. In our case, the two objectives are the minimization of makespan and total flowtime. These problems are solved with a novel multiobjective local search framework-based decomposition, called multiobjective local search based decomposition (MOLSD), which decomposes a multiobjective problem into a number of single objective optimization subproblems using aggregation method and optimizes them simultaneously. First, a problem-specific Nawaz–Enscore–Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a Pareto local search embedded with a heavy perturbation operator is applied to search the promising neighbors of each nondominated solution found so far. Then, when solving each subproblem, a single insert-based local search, a multiple local search strategy, and a doubling perturbation mechanism are designed to exploit the new individual. Finally, a restarted method is used to avoid the algorithm trapping into the local optima, which has a significant effect on the performance of the MOLSD. Comprehensive experiments have been conducted by two standard multiobjective metrics: 1) hyper-volume indicator; and 2) set coverage. The experimental results show that the proposed MOLSD provides better solutions that several state of the art algorithms.

Multiobjective Local Search Algorithm-Based Decomposition for Multiobjective Permutation Flow Shop Scheduling Problem

Microarray cancer gene expression datasets are high dimensional and complex for efficient computational analysis. Thus, selecting high discriminative genes from microarray data has become increasingly interesting in the field of bioinformatics. This article addresses the problem of designing a gene selection algorithm which is modeled as a multiobjective optimization problem with the sensitivity, specificity and the number of selected genes. Then, a multi-objective ranking binary artificial bee colony algorithm based on decomposition is proposed to select the optimal subset of dimensions from the original high dimensional data while retaining a subset that satisfies the defined objective. First, the Fisher-Markov selector is used to choose a fixed number of microarray data. Second, to make artificial bee colony algorithm suitable for the binary problem, a novel binary update strategy is proposed to balance the exploration and exploitation ability. Third, multi-objective ranking binary artificial bee colony algorithm is proposed by integrating tchebycheff approach into the ranking binary artificial bee colony algorithm. Finally, the multi-objective ranking binary artificial bee colony algorithm based on decomposition (MORBABC/D) method is used for feature selection, and extreme learning machine is used as the classifier with 10 fold cross-validation method. In order to show the effectiveness and efficiency of the algorithm, the proposed algorithm is tested on eight microarray dataset. Experimental studies have been carried out to investigate the ability of the proposed algorithm.

Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets

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Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times

Multiobjective Local Search Algorithm-Based Decomposition for Multiobjective Permutation Flow Shop Scheduling Problem

Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets