Evolutionary Simulated Annealing Research Articles

Core design optimization of a 200 MWt pressurized water SMR using evolutionary simulated annealing

The paper presents the design optimization of a pressurized small modular reactor (SMR) core using evolutionary simulated annealing (ESA). The core was designed for the power output of 200 MWt based on the typical 17 × 17 fuel assemblies of a traditional PWR. The core consists of 37 fuel assemblies of F235, F340 and F445 types corresponding to the 235U enrichment levels of 2.35, 3.40 and 4.45 wt%, respectively. The design target is to attain the fuel cycle length of about 900 effective full power days (EFPDs) with a minimum core average 235U enrichment, while flattening the radial power distribution. Lattice and core physics calculations were performed using the SRAC code system. The COREBN module of the SRAC code system was coupled with the ESA method for performing the core burnup calculations during the optimization process. A fitness function was chosen to combine the objectives of the cycle length, the core average enrichment and the radial power peaking factor (PPF). A survey was conducted for determining the optimal control parameters of the ESA method and the weighting factors in the fitness function associated to the design objectives. The optimal core was selected from the ESA search consisting of 21 assemblies of F235, 8 of F340 and 8 of F445 type, respectively. The core average 235U enrichment is 3.031 wt% with the cycle length of about 840 EFPDs. The PPF of the selected core is 1.428 which appears at the centered assembly.

Automatic Timetable Generation

Abstract: Generating a timetable manually for educational institutions can be a tedious task for educators, as each grade has a predefined education scheme and faculty workload that needs to be taken into account. The challenge lies in preparing a timetable that does not overlap with the schedules of the faculty members and can efficiently utilize all available resources. To overcome these difficulties, our project proposes the use of algorithms such as Evolutionary Algorithm, Tabu Search, Simulated Annealing, and scatter search. The proposed system will take inputs such as grade-wise subjects, teachers, and workload, and generate a possible timetable for the working days of the week. The system will integrate these inputs to make optimal use of all resources while adhering to the predefined constraints. This will help overcome the constraints that appear in the manual timetable generation process, resulting in timetables for any number of courses and grades. The implementation of this proposed system will provide a dynamic solution with the best approach, making it easier for educationalists to manage and set timetables. It will eliminate the complexity of manually generating and managing timetables, providing an efficient and effective solution to this challenging task

Topology Optimization Based on SA-BESO

Bidirectional asymptotic structure methods have long been used to solve topological optimization problems, but are prone to being stuck in local optimal solutions. To solve this problem, this paper proposed a topology optimization method based on the Bi-directional Evolutionary structure Structural Optimization and Simulated Annealing algorithm (SA-BESO). First, the structural elements of the structural partition are encoded by a dual encoding, where elements are assigned with density values and binary strings. Second, binary strings are crossed and mutated, while criteria for adding and removing structural units are formulated. Then, structures are updated randomly. Finally, the structural compliance of the current structure is evaluated. If the structural compliance of the original structure increases, it will be accepted with a certain probability, thus jumping out of the local optimal solution. Related examples show that the SA-BESO method improves the smoothness of the optimization process and can obtain optimized structures with lower structural compliance and computational cost.

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Hybrid Evolutionary Strategy and Simulated Annealing Algorithms for Integrated Process Planning, Scheduling and Due-Date Assignment Problem

Although integrated process planning and scheduling (IPPS) problem is studied extensively as scheduling with due-date assignment problem (SWDDA) in the literature, there are only a few studies on integration of these three manufacturing functions which are process planning, scheduling, and due-date assignment. Since outputs of upper stream functions effect downstream functions and higher integration gives a better global performance, it is better to integrate these three functions. In this study integration of process planning and earliest due-date scheduling (EDD) with due-date assignment is studied using hybrid evolutionary strategies (RS/ES) and hybrid simulated annealing algorithms (RS/SA). Ordinary solutions (OS), random search (RS) solutions, evolutionary strategies (ES) solutions, and simulated annealing (SA) solutions are compared with each other and hybrid solutions of ES and SA, with RS. According to the results, higher integration found better and best results are obtained with the highest integration level. ES and SA methods gave better results compared to the RS and OS, and RS/ES and RS/SA techniques were found promising search techniques.

Application of Evolutionary Simulated Annealing Method to Design a Small 200 MWt Reactor Core

This paper presents the application of an evolutionary simulated annealing (ESA) method to design a small 200 MWt reactor core. The core design is based on a reference ACPR50 reactor deployed in a floating nuclear power plant. The core consists of 37 typical 17x17 PWR fuel assemblies with three different U-235 enrichments of 4.45, 3.40 and 2.35 wt%. Core loading pattern (LP) has been optimized for obtaining the cycle length of 900 effective full power days, while minimizing the average U-235 enrichment and the radial power peaking factor. The optimization process was performed by coupling the ESA method with the COREBN module of the SRAC2006 system code.

Evolutionary simulated annealing for fuel loading optimization of VVER-1000 reactor

An evolutionary simulated annealing (ESA) method has been developed for the problem of fuel loading optimization of VVER-1000 reactor. The ESA method improves original simulated annealing by using crossover and mutation operators to generate new trial loading patterns (LPs). A core physics calculation code for fuel LP optimization of VVER reactors (LPO-V) has been developed and verified based on a VVER-1000 MOX benchmark core in comparison with MCNP4c calculations. Calculations for optimizing fuel LP of the VVER-1000 MOX core have been conducted using the ESA method in comparison with simulated annealing (SA) and adaptive simulated annealing (ASA). Statistical differences between these methods were also evaluated based on the Mann–Whitney U test. The results show that the ESA method is advantageous over the SA and ASA.

Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

Parallel genetic algorithm for N‐Queens problem based on message passing interface‐compute unified device architecture

AbstractN‐Queens problem derives three variants: obtaining a specific solution, obtaining a set of solutions and obtaining all solutions. The purpose of the variant I is to find a constructive solution, which has been solved. Variant III is aiming to find all solutions and the largest number of queens currently being resolved is 26. Variant II whose purpose is to obtain a set of solutions for larger‐scale problems relies on various intelligent algorithms. In this paper, we use a master‐slave model genetic algorithm that combines the idea of the evolutionary algorithm and simulated annealing algorithm to solve Variant III, and use a parallel fitness function based on compute unified device architecture. Experimental results show that our scheme achieved a maximum 60‐fold speedup over the single‐CPU counterpart. On this basis, a two‐level parallel genetic algorithm based on the island model and master‐slave model is implemented on the GPU cluster by using message passing interface technology. Using two‐node and three‐node GPU cluster, speedup of 1.46 and 2.01 are obtained on average over single‐node, respectively. Compared with the sequential genetic algorithm, the two‐level parallel genetic algorithm makes full use of the parallel computing power of GPU cluster in solving N‐Queen variant II and improves the performance by 99.19 times in the best case.

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.

Towards the democratisation of design: the implementation of metaheuristic search strategies to enable the auto-assignment of manufacturing parameters for FDM

Emerging manufacturing technologies such as Filament Deposition Modelling (FDM) printing have the ability to democratise manufacturing by enabling the low-cost production of goods in homes and communities. In addition to changing the way we manufacture, FDM and other Additive Manufacturing (AM) technologies are also able to revolutionise the way in which we design, as they permit the creation of structures not permissible by traditional subtractive processes. This flexibility also however creates a large and complex solution space yielding the generation of optimal designs incredibly difficult.A design methodology is proposed that accounts for the large and complex solution space afforded by the FDM process. It combines an empirically-derived capability profile of the FDM manufacturing process and a fusion of activities from both virtual and physical design processes. Capability profiles coupled with classical analysis techniques are used to predict part behaviours’ and, when coupled with appropriate solution space navigation, enable the auto-generation of manufacturing parameters & geometries that can satisfy the functional requirements of an object.This paper presents the Universal Hook Generator - an instantiation of this design methodology applied to the design of a generic load bearing component. It is used as a platform to explore the suitability of three metaheuristic search techniques; Evolutionary Algorithms, Simulated Annealing and Particle Swarm Optimisation. Each is assessed with respect their ability to auto-generate manufacturing and geometric parameters of a load bearing hook. Particle Swarm Optimisation is found to outperform both Evolutionary Algorithms and Simulated Annealing with respect to both quality and consistency of generated solutions.

Optimal pipe inspection paths considering inspection tool limitations

The inspection of deteriorating water distribution pipes is an important process for utilities. It helps them gain a better understanding of the condition of their buried conveyance systems and aids better decision making for risk-based asset management. In-pipe continuous inspection tools provide high resolution and accurate data, but they have seen relatively limited use due to cost and operational constraints. To facilitate-cost efficient deployment of these technologies and maximal information gain, a process that finds high risk pipes to inspect while accounting for the limitations of the tools at hand is needed. This paper shows how to incorporate these considerations within an optimization formulation, and examines the use of Evolutionary Programming, Simulated Annealing, and Greedy Search heuristics to identify inspection paths. Case studies performed on both synthetic and real world networks demonstrate that Evolutionary Programs are the most effective. While only three factors are used to characterize tool limitations, the method presented in this paper can be extended to include technology-specific complexities in real world applications.

A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem

The water cycle algorithm (WCA) is a nature-inspired meta-heuristic recently contributed to the community in 2012, which finds its motivation in the natural surface runoff phase in water cycle process and on how streams and rivers flow into the sea. This method has been so far successfully applied to many engineering applications, spread over a wide variety of application fields. In this paper an enhanced discrete version of the WCA (coined as DWCA) is proposed for solving the Symmetric and Asymmetric Traveling Salesman Problem. Aimed at proving that the developed approach is a promising approximation method for solving this family of optimization problems, the designed solver has been tested over 33 problem datasets, comparing the obtained outcomes with the ones got by six different algorithmic counterparts from the related literature: genetic algorithm, island-based genetic algorithm, evolutionary simulated annealing, bat algorithm, firefly algorithm and imperialist competitive algorithm. Furthermore, the statistical significance of the performance gaps found in this benchmark is validated based on the results from non-parametric tests, not only in terms of optimality but also in regards to convergence speed. We conclude that the proposed DWCA approach outperforms – with statistical significance – any other optimization technique in the benchmark in terms of both computation metrics.

A multimetric and multideme multiagent system for multiobjective optimization

AbstractThis article proposes a multiagent system consisting of a number of multiobjective metaheuristic agents (namely, multiobjective genetic algorithm, strength Pareto evolutionary algorithm, differential evolution, simulated annealing, and particle swarm optimization) working toward to extract optimal or very close‐to‐optimal Pareto fronts using multiple performance metrics in a sessionwise manner. At the beginning of each session, the main population is divided into a number of subpopulations, and each of them is assigned to a particular agent. The system runs in consecutive sessions such that, at the beginning of a session, agents start running after being assigned with a subpopulation and return the optimized subpopulations together with the corresponding set of nondominated solutions at the end of the session. There are 3 multiobjective assessment metrics in use, and a different metric is considered for each session to measure the success of each metaheuristic agent. The evaluation of individual agents using a particular assessment metric is used in 2 ways: first, the number of fitness evaluations for each agent is adjusted based on their performance; second, the subpopulation improved by an individual agent might be rejected on the basis of its evaluation score. At the end of each session, individual subpopulations are merged to get the updated main population, whereas individual sets of nondominated solutions are combined to form the global Pareto front. In addition to the individual multiobjective metaheuristic agents, the system also contains a number of coordination and synchronization agents that run the whole system toward its objectives. The proposed system is tested using real‐valued multiobjective benchmark problems in 2009 IEEE Congress on Evolutionary Computation. Experimental results and statistical evaluations exhibited that the achieved success is better than many of state‐of‐the‐art algorithms.

A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection

Abstract The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.

A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

Purpose The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost. Design/methodology/approach A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search. Findings Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space. Research limitations/implications This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured. Practical implications The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases. Originality/value The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.

A Combination of the Evolutionary Tree Miner and Simulated Annealing

In recent years, process mining is important to discover process model from event logs; however the existing methods have not achieved good in overall fitness. In this context, this paper proposes a combination of the Evolutionary Tree Miner (ETM) and Simulated Annealing (SA). The ETM aims to reduce randomness of population so that it can improved the quality of individuals. SA aims to increase overall fitness in the population. The results of the proposed method which was compared to other approaches show that the proposes method had better in overall fitness and better quality of individuals.

A NEW HYBRID YIN-YANG-PAIR-PARTICLE SWARM OPTIMIZATION ALGORITHM FOR UNCAPACITATED WAREHOUSE LOCATION PROBLEMS

Abstract. Yin-Yang-pair optimization (YYPO) is one of the latest metaheuristic algorithms (MA) proposed in 2015 that tries to inspire the philosophy of balance between conflicting concepts. Particle swarm optimizer (PSO) is one of the first population-based MA inspired by social behaviors of birds. In spite of PSO, the YYPO is not a nature inspired optimizer. It has a low complexity and starts with only two initial positions and can produce more points with regard to the dimension of target problem. Due to unique advantages of these methodologies and to mitigate the immature convergence and local optima (LO) stagnation problems in PSO, in this work, a continuous hybrid strategy based on the behaviors of PSO and YYPO is proposed to attain the suboptimal solutions of uncapacitated warehouse location (UWL) problems. This efficient hierarchical PSO-based optimizer (PSOYPO) can improve the effectiveness of PSO on spatial optimization tasks such as the family of UWL problems. The performance of the proposed PSOYPO is verified according to some UWL benchmark cases. These test cases have been used in several works to evaluate the efficacy of different MA. Then, the PSOYPO is compared to the standard PSO, genetic algorithm (GA), harmony search (HS), modified HS (OBCHS), and evolutionary simulated annealing (ESA). The experimental results demonstrate that the PSOYPO can reveal a better or competitive efficacy compared to the PSO and other MA.

Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management.

Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem

This paper investigates the Google machine reassignment problem (GMRP). GMRP is a real world optimisation problem which is to maximise the usage of cloud machines. Since GMRP is computationally challenging problem and exact methods are only advisable for small instances, meta-heuristic algorithms have been used to address medium and large instances. This paper proposes a cooperative evolutionary heterogeneous simulated annealing (CHSA) algorithm for GMRP. The proposed algorithm consists of several components devised to generate high quality solutions. Firstly, a population of solutions is used to effectively explore the solution space. Secondly, CHSA uses a pool of heterogeneous simulated annealing algorithms in which each one starts from a different initial solution and has its own configuration. Thirdly, a cooperative mechanism is designed to allow parallel searches to share their best solutions. Finally, a restart strategy based on mutation operators is proposed to improve the search performance and diversification. The evaluation on 30 diverse real-world instances shows that the proposed CHSA performs better compared to cooperative homogeneous SA and heterogeneous SA with no cooperation. In addition, CHSA outperformed the current state-of-the-art algorithms, providing new best solutions for eleven instances. The analysis on algorithm behaviour clearly shows the benefits of the cooperative heterogeneous approach on search performance.