Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem

Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features

Implementasi Algoritma Firefly pada Kasus N-Queens Problem

N-Queen problem is a form of puzzle game that uses chess rules for the queen on the standard chessboard with modified size. The challenge of the n-queen problem is finding the N ( N is positive integer) queens position on the chessboard, so that no queen can attack another queen on the board in a single move. Implementation of firefly algorithm in n-queens problem in this study aims to find n-queen problem solutions and count the number of iterations to achieve the optimal solution of each queen which will then be compared with the results of Sarkar and Nag's research (2017). This study uses an experimental method with a number of N between 10 to 20 and uses a population of 15 and 1000 firefly. The results showed that the firefly algorithm is able to find all the optimal solutions for the queen's position on a chessboard with dimensions 10 to 20 in a population of 1000 firefly. The firefly algorithm can find the optimal solution fewer iterations compared to the genetic algorithm. According to the experiment, firefly algorithm shows better performance in finding the optimal solution compared to genetic algorithm.-Queen problem adalah bentuk permainan menggunakan aturan permain catur pada papan catur standard namun ukurannya dapat dimodifikasi menjadi lebih besar. Tantangan dari n-queen problem adalah bagaimana menempatkan n (n adalah bilangan bulat positif) buah queen pada papan catur berukuran nxn, dimana setiap queen tidak boleh saling memakan dengan hanya 1 langkah. Queens dapat memakan dengan arah vertical horizontal ataupun diagonal baik maju ataupun mundur satu langkah. Implementasi algoritma firefly pada n-queens problem dalam penelitian ini bertujuan untuk menemukan solusi n-queen problem berdasarkan aturan n-queen lalu menghitung jumlah iterasi yang diperoleh dari setiap solusi optimal masing-masing n ratu tersebut yang kemudian akan dikomparasikan dengan hasil penelitian Sarkar dan Nag (2017). Penelitian ini menggunakan metode eksperimen dengan jumlah N antara 10 sampai dengan 20 dan menggunakan populasi 15 dan 1000 firefly. Hasil yang diperoleh menunjukkan bahwa algoritma firefly mampu menemukan semua solusi optimal posisi ratu pada papan catur dengan dimensi 10 sampai dengan 20 pada populasi 1000 firefly. Algoritma firefly dapat menemukan solusi optimal dengan jumlah iterasi lebih sedikit dibanding dengan algoritma genetik, dengan kata lain bahwa algoritma firefly lebih baik dibanding dengan algoritma genetik dilihat dari jumlah iterasi yang dibutuhkan pada kasus yang sama

INTEGRATED APPROACH OF SCHEDULING A FLEXIBLE JOB SHOP USING ENHANCED FIREFLY AND HYBRID FLOWER POLLINATION ALGORITHMS

Manufacturing industries are undergoing tremendous transformation due to Industry 4.0. Flexibility, consumer demands, product customization, high product quality, and reduced delivery times are mandatory for the survival of a manufacturing plant, for which scheduling plays a major role. A job shop problem modified with flexibility is called flexible job shop scheduling. It is an integral part of smart manufacturing. This study aims to optimize scheduling using an integrated approach, where assigning machines and their routing are concurrently performed. Two hybrid methods have been proposed: 1) The Hybrid Adaptive Firefly Algorithm (HAdFA) and 2) Hybrid Flower Pollination Algorithm (HFPA). To address the premature convergence problem inherent in the classic firefly algorithm, the proposed HAdFA employs two novel adaptive strategies: employing an adaptive randomization parameter (α), which dynamically modifies at each step, and Gray relational analysis updates firefly at each step, thereby maintaining a balance between diversification and intensification. HFPA is inspired by the pollination strategy of flowers. Additionally, both HAdFA and HFPA are incorporated with a local search technique of enhanced simulated annealing to accelerate the algorithm and prevent local optima entrapment. Tests on standard benchmark cases have been performed to demonstrate the proposed algorithm’s efficacy. The proposed HAdFA surpasses the performance of the HFPA and other metaheuristics found in the literature. A case study was conducted to further authenticate the efficiency of our algorithm. Our algorithm significantly improves convergence speed and enables the exploration of a large number of rich optimal solutions.

A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS)

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions

An enhanced supplier selection model based on optimized analytic network process towards sustainable information technology outsourcing

Information Technology Outsourcing (ITO) has become part of the organization’s strategy as it offers benefits such as high-quality products, cost reduction, and increased productivity. Essentially, ITO is a complex process in which selecting the right supplier involves evaluation of multi criteria. To ensure the sustainable of the ITO project, the evaluation criteria should consider risk factors and other sustainability criteria of the project. However, existing ITO supplier selection models lack of sustainability criteria and risk factors. Moreover, these methods rely on human judgment in weight allocation. Therefore, this study proposes an Enhanced Supplier Selection Model (ESS) for sustainable ITO mainly to eliminate human judgment in Analytical Network Process (ANP) method. The ESS Model was constructed through theoretical, exploratory and experimental studies. The exploratory study was carried in Thailand using survey which involved 45 respondents. Findings from the study was used to construct evaluation criteria and become datasets for ESS. The proposed ESS Model was evaluated using expert reviews and case studies in Thailand. The ESS model contains two main components: evaluation criteria and a decision-making method. The first has nineteen (19) sustainability criteria and seven (7) risk factors. While the latter is an enhanced ANP with Firefly Algorithm (ANP-FA). The evaluation results indicate that the Consistency Ratio (CR) for ANP-FA is smaller than ANP, which is 0.003 compared to 0.031. This outcome shows that the ESS model is feasible in removing human judgment in supplier selection of ITO projects. The study’s contributions can be interpreted from two perspectives. The proposed ESS model is a theoretical contribution in Multi-Criteria Decision-Making and Supplier Selection in ITO project. In terms of practicality, the model has been realized in Thailand organizations to ensure the sustainability of ITO projects