Kazi Shah Nawaz Ripon

A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization

Abstract This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749–768; T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber repeater placement using a jumping gene algorithm, in: Proceedings of the IEEE International Conference on Industrial Technology (ICIT 2005), Hong Kong, 2005, pp. 291–296; K.F. Man, T.M. Chan, K.S. Tang, S. Kwong, Jumping-genes in evolutionary computing, in: Proceedings of the IEEE IECON’2004, Busan, 2004, pp. 1268–1272]. JGGA is a relatively new multiobjective evolutionary algorithm (MOEA) that imitates a jumping gene phenomenon discovered by Nobel Laureate McClintock during her work on the corn plants. The main feature of JGGA is that it only has a simple operation in which a transposition of gene(s) is induced within the same or another chromosome in the genetic algorithm (GA) framework. In its initial formulation, the search space solutions are binary-coded and it inherits the customary problems of conventional binary-coded GA (BCGA). This issue motivated us to remodel the JGGA into RJGGA. The performance of RJGGA has been compared to other MOEAs using some carefully chosen benchmark test functions. It has been observed that RJGGA is able to generate non-dominated solutions with a wider spread along the Pareto-optimal front and better address the issues regarding convergence and diversity in multiobjective optimization.

Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities

Abstract In this paper, we report the results of our investigation of an evolutionary approach for solving the unequal area multi-objective facility layout problem (FLP) using the variable neighborhood search (VNS) with an adaptive scheme that presents the final layouts as a set of Pareto-optimal solutions. The unequal area FLP comprises a class of extremely difficult and widely applicable optimization problems arising in diverse areas and meeting the requirements for real-world applications. The VNS is an explorative local search method whose basic idea is systematic change of neighborhood within a local search. Traditionally, local search is applied to the solutions of each generation of an evolutionary algorithm, and has often been criticized for wasting computation time. To address these issues, the proposed approach is composed of the VNS with a modified 1 -opt local search, an extended adaptive local search scheme for optimizing multiple objectives, and the multi-objective genetic algorithm (GA). Unlike conventional local search, the proposed adaptive local search scheme automatically determines whether the VNS is used in a GA loop or not. We investigate the performance of the proposed approach in comparison to multi-objective GA-based approaches without local search and augmented with traditional local search. The computational results indicate that the proposed approach with adaptive VNS is more efficient in most of the performance measures and can find near-optimal layouts by optimizing multiple criteria simultaneously.

Evolutionary multi-objective clustering for overlapping clusters detection

Evolutionary algorithms have a history of being applied into clustering analysis. However, most of the existing evolutionary clustering techniques fail to detect complex/spiral-shaped clusters. In our previous works, we proposed several evolutionary multi-objective clustering algorithms and achieved promising results. Still, they suffer from this usual problem exhibited by evolutionary and unsupervised clustering approaches. In this paper, we proposed an improved multi-objective evolutionary clustering approach (EMCOC) to resolve the overlapping problems in complex shape data. Experimental results based on several artificial and real-world data show that the proposed EMCOC can successfully identify overlapping clusters. It also succeeds obtaining non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance. The superiority of the EMCOC over some other multi-objective evolutionary clustering algorithms is also confirmed by the experimental results.

Multi-Objective Evolutionary Job-Shop Scheduling Using Jumping Genes Genetic Algorithm

The job-shop scheduling problem (JSSP) is a hard combinatorial optimization problem. Several evolutionary approaches have been proposed to solve JSSP. But most of them are limited to single objective and fail in real-world applications, which naturally involve multiple objectives. In this paper, we pretend evolutionary approach for solving multi-objective JSSP using jumping genes genetic algorithm (JGGA) that heuristically searches for the near-optimal solutions optimizing multiple criteria simultaneously. Experimental results reveal that our proposed approach can search for the near-optimal solutions by optimizing multiple criteria and also capable of finding a set of diverse and nondominated scheduling solutions.

Multi-Objective Data Clustering using Variable-Length Real Jumping Genes Genetic Algorithm and Local Search Method

In this paper, we present a novel multi-objective evolutionary clustering approach using variable-length real jumping genes genetic algorithms (VRJGGA). The proposed algorithm that extends jumping genes genetic algorithm (JGGA) [1] evolves clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. Some local search methods such as probabilistic cluster merging and splitting are introduced in VRJGGA for the clustering improvement. Experimental results based on several artificial and real-world data show that VRJGGA can obtain non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance.

Improved precedence preservation crossover for multi-objective job shop scheduling problem

Over the last three decades, a great deal of research has been focused on solving the job-shop scheduling problem (JSSP). Researchers have emerged with a wide variety of approaches to solve this stubborn problem. Recently much effort has been concentrated on evolutionary techniques to search for the near-optimal solutions optimizing multiple criteria simultaneously. The choice of crossover operator is very important in the aspect of genetic algorithms (GA), and consequently a wide range of crossover operators have been proposed for JSSP. Most of them represent a solution by a chromosome containing the sequence of all the operations and decode the chromosome to a real schedule from the first gene to the last gene. However, these methods introduce high redundancy at the tail of the chromosome. In this paper, we address this problem in case of precedence preservation crossover (PPX) which is regarded as one of the better crossover operators and propose an improved version, termed as improved precedence preservation crossover (IPPX). Experimental results reveal that our proposed approach finds the near-optimal solutions by optimizing multiple criteria simultaneously with better results and also reduces the execution time significantly.

Pareto Optimal Based Evolutionary Approach for Solving Multi-Objective Facility Layout Problem

Over the years, various evolutionary approaches have been proposed in efforts to solve the facility layout problem (FLP). Unfortunately, most of these approaches are limited to a single objective only, and often fail to meet the requirements for real-world applications. To date, there are only a few multi-objective FLP approaches have been proposed. However, they are implemented using weighted sum method and inherit the customary problems of this method. In this paper, we propose an evolutionary approach for solving multi-objective FLP using multi-objective genetic algorithm that presents the layout as a set of Pareto optimal solutions optimizing both quantitative and qualitative objective simultaneously. Experimental results obtained with the proposed algorithm on test problems taken from the literature are promising.

Using pareto-optimality for solving multi-objective unequal area facility layout problem

A lot of optimal and heuristic algorithms for solving facility layout problem (FLP) have been developed in the past few decades. The majority of these approaches adopt a problem formulation known as the quadratic assignment problem (QAP) that is particularly suitable for equal area facilities. Unequal area FLP comprises a class of extremely difficult and widely applicable optimization problems arising in many diverse areas to meet the requirements for real-world applications. Unfortunately, most of these approaches are based on a single objective. While, the real-world FLPs are multi-objective by nature. Only very recently have meta-heuristics been designed and used in multi-objective FLP. They most often use the weighted sum method to combine the different objectives and thus, inherit the well-known problems of this method. As of now, there is no formal approach published for the unequal area multi-objective FLP to consider several objectives simultaneously. This paper presents an evolutionary approach for solving multi-objective unequal area FLP using multi-objective genetic algorithm that presents the layout as a set of Pareto-optimal solutions optimizing multiple objectives simultaneously. The experimental results show that the proposed approach performs well in dealing with multi-objective unequal area FLPs which better reflects the real-world scenario.

Dynamic facility layout problem under uncertainty: a Pareto-optimality based multi-objective evolutionary approach

In this paper, we investigate an evolutionary approach to solve the multi-objective dynamic facility layout problem (FLP) under uncertainty that presents the layout as a set of Pareto-optimal solutions. Research examining the dynamic FLP usually assumes that data for each time period are deterministic and known with certainty. However, production uncertainty is one of the most challenging aspects in today’s manufacturing environments. Researchers have only recently modeled FLPs with uncertainty. Unfortunately, most solution methodologies developed to date for both static and dynamic FLPs under uncertainty focus on optimizing just a single objective. To the best of our knowledge, the use of Pareto-optimality in multi-objective dynamic FLPs under uncertainty has not yet been studied. In addition, the approach proposed in this paper is tested using a backward pass heuristic to determine its effectiveness in optimizing multiple objectives. Results show that our approach is an efficient evolutionary dynamic FLP approach to optimize multiple objectives simultaneously under uncertainty.

Integrated job shop scheduling and layout planning: a hybrid evolutionary method for optimizing multiple objectives

The facility layout planning (FLP) and the job shop scheduling problem (JSSP) are two major design issues that impact on the efficiency and productivity of manufacturing systems. The interactions between these two combinatorial optimization problems are widely known. Although, a great deal of research has been focused on solving these problems, relatively few techniques have been developed for solving them as an inter-dependent problem, none of which consider multiple objectives to better reflect practical manufacturing scenarios. Also, traditional approaches do not consider the transportation delay between two consecutive operations while solving JSSPs. Focusing on the autonomy of the manufacturing environment, this paper presents a multi-objective evolutionary method for solving JSSP that considers transportation delays and FLP as an integrated problem, which presents the final solutions as a Pareto-optimal set. In this research, a hybrid genetic algorithm by incorporating variable neighborhood search is applied to simultaneously optimize makespan and mean flow time for JSSPs, as well as total material handling cost and closeness rating scores for FLPs. This is an extension to the authors’ previous work.