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.

Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

The flower pollination algorithm (FPA) is anefficient metaheuristicoptimizationalgorithm mimickingthe pollinationprocessof flowering species. In this study, FPA is applied, for first time, to the optimum design of reinforced concrete (RC) cantilever retaining walls. It is foundthat FPA offers important savings with respect to conventional design approachesand that it outperformsgenetic algorithm (GA)andthe particle swarm optimization (PSO) algorithm in this designproblem.Furthermore, parameter tuning reveals that the best FPA performance is achieved for switch probability values ranging between 0.4 and 0.7, a population size of 20 individualsand aLévy flightstep sizescale factor of 0.5. Finally, parametric optimum designs show that theoptimumcost of RC retaining walls increases rapidly with the wallheight and smoothly with the magnitude of surcharge loadin

An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS),using the t -way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance

Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach

Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently

Global convergence analysis of the flower pollination algorithm: a Discrete-Time Markov Chain Approach

Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

A Comprehensive Review of Recent Variants and Modifications of Firefly Algorithm

Swarm intelligence (SI) is an emerging field of biologically-inspired artificial intelligence based on the behavioral models of social insects such as ants, bees, wasps, termites etc. Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. In the last two decades, there has been a growing interest of addressing Dynamic Optimization Problems using SI algorithms due to their adaptation capabilities. This paper presents a broad review on two SI algorithms: 1) Firefly Algorithm (FA) 2) Flower Pollination Algorithm (FPA). FA is inspired from bioluminescence characteristic of fireflies. FPA is inspired from the the pollination behavior of flowering plants. This article aims to give a detailed analysis of different variants of FA and FPA developed by parameter adaptations, modification, hybridization as on date. This paper also addresses the applications of these algorithms in various fields. In addition, literatures found that most of the cases that used FA and FPA technique have outperformed compare to other metaheuristic algorithms