scholarly journals Integration of Ordinal Optimization with Ant Lion Optimization for Solving the Computationally Expensive Simulation Optimization Problems

2020 ◽  
Vol 11 (1) ◽  
pp. 136
Author(s):  
Shih-Cheng Horng ◽  
Chin-Tan Lee
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Solution Space ◽  
Multivariate Adaptive Regression Splines ◽  
Ordinal Optimization ◽  
Ant Lion Optimization ◽  
Short Period ◽  
Ant Lion ◽  
Computationally Expensive

The optimization of several practical large-scale engineering systems is computationally expensive. The computationally expensive simulation optimization problems (CESOP) are concerned about the limited budget being effectively allocated to meet a stochastic objective function which required running computationally expensive simulation. Although computing devices continue to increase in power, the complexity of evaluating a solution continues to keep pace. Ordinal optimization (OO) is developed as an efficient framework for solving CESOP. In this work, a heuristic algorithm integrating ordinal optimization with ant lion optimization (OALO) is proposed to solve the CESOP within a short period of time. The OALO algorithm comprises three parts: approximation model, global exploration, and local exploitation. Firstly, the multivariate adaptive regression splines (MARS) is adopted as a fitness estimation of a design. Next, a reformed ant lion optimization (RALO) is proposed to find N exceptional designs from the solution space. Finally, a ranking and selection procedure is used to decide a quasi-optimal design from the N exceptional designs. The OALO algorithm is applied to optimal queuing design in a communication system, which is formulated as a CESOP. The OALO algorithm is compared with three competing approaches. Test results reveal that the OALO algorithm identifies solutions with better solution quality and better computing efficiency than three competing algorithms.

COMBINING EVOLUTION STRATEGY WITH ORDINAL OPTIMIZATION

2013 ◽  
Vol 12 (02) ◽  
pp. 233-260 ◽  
Author(s):  
SHIH-CHENG HORNG ◽  
SHIN-YEU LIN
Keyword(s):  
Real Time ◽  
Stochastic Simulation ◽  
Surrogate Model ◽  
Optimization Problems ◽  
Simulation Optimization ◽  
Solution Space ◽  
Evolution Strategy ◽  
Ordinal Optimization ◽  
Discrete Solution ◽  
Testing Problem

In this paper, we combine evolution strategy (ES) with ordinal optimization (OO), abbreviated as ES + OO, to solve real-time combinatorial stochastic simulation optimization problems with huge discrete solution space. The first step of ES + OO is to use an artificial neural network (ANN) to construct a surrogate model to roughly evaluate the objective value of a solution. In the second step, we apply ES assisted by the ANN-based surrogate model to the considered problem to obtain a subset of good enough solutions. In the last step, we use the exact model to evaluate each solution in the good enough subset, and the best one is the final good enough solution. We apply the proposed algorithm to a wafer testing problem, which is formulated as a combinatorial stochastic simulation optimization problem that consists of a huge discrete solution space formed by the vector of threshold values in the testing process. We demonstrate that (a) ES + OO outperforms the combination of genetic algorithm (GA) with OO using extensive simulations in the wafer testing problem, and its computational efficiency is suitable for real-time application, (b) the merit of using OO approach in solving the considered problem and (c) ES + OO can obtain the approximate Pareto optimal solution of the multi-objective function resided in the considered problem. Above all, we propose a systematic procedure to evaluate the performance of ES + OO by providing a quantitative result.

scholarly journals Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems

2020 ◽  
Vol 10 (6) ◽  
pp. 2075 ◽  
Author(s):  
Shih-Cheng Horng ◽  
Shieh-Shing Lin
Keyword(s):  
Optimization Problems ◽  
Solution Space ◽  
Inequality Constraints ◽  
Optimization Methods ◽  
Ordinal Optimization ◽  
Np Hard ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Hard Problems ◽  
Np Hard Problems

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods.

Redundancy optimization for multi-state series-parallel systems using ordinal optimization-based-genetic algorithm

2021 ◽  
pp. 1748006X2110286
Author(s):  
Yishuang Hu ◽  
Yi Ding ◽  
Zhiguo Zeng
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Real Life ◽  
Parallel Systems ◽  
Function Technique ◽  
Ordinal Optimization ◽  
Engineering Systems ◽  
Designing Method ◽  
Redundancy Optimization

Multi-state series-parallel systems (MSSPSs) are widely-used for representing engineering systems. In real-life cases, engineers need to design an optimal MSSPS structure by combining different versions and number of redundant components. The objective of the design is to ensure reliability requirements using the least costs, which could be formulated as a redundancy optimization problem under reliability constraints. The genetic algorithm is one of the most frequently used method for solving redundancy optimization problems. In traditional genetic algorithms, the population size needs to be determined based on the experience of the modeler. Often, this ends up creating a large number of unnecessary samples. As a result, the computational burden can be huge, especially for large-scale MSSPS structures. To solve these problems, this paper proposes an optimal structure designing method named as redundancy ordinal optimization. The universal generating function technique is applied to evaluate the reliabilities of the MSSPSs. Based on the reliabilities, an ordinal optimization algorithm is adapted to update the parent populations and the stopping criterion of genetic algorithm, so that the unnecessary structure designs can be eliminated. Numerical examples show that the proposed method improves the computational efficiency while remaining satisfactorily accurate.

scholarly journals An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pooja Verma ◽  
Raghav Prasad Parouha
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Structural Engineering ◽  
Large Scale ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Solution Space ◽  
Selection Strategy ◽  
Engineering Design Optimization ◽  
Global And Local

AbstractAn advanced hybrid algorithm (haDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The performance of proposed haDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)-scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.

scholarly journals Economic Load Dispatch

2021 ◽  
Vol 9 (VI) ◽  
pp. 1646-1651
Author(s):  
Prof. Kanika Lamba
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

ELD or Economic load dispatch is an online process of allocating generating among the available generating units to minimize the total generating cost and satisfy the equality and inequality constraint. ELD means the real and reactive power of the generator vary within the certain limits and fulfils theload demand with less fuel cost. There are some traditional methods for = 1; 2; :::;N) isgiven as Vi=[Vi;1; Vi;2; :::; Vi;D]. The index ivaries from solving ELD include lambda irritation method, Newton-Raphson method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimization problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimization), ALO ( ant-lion optimization), solve non convex optimization problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, ALO (ant-lion Optimization) and PSO (Particle Swarm Optimization) and the results have been compared. All the analyses have been made in MATLAB environment

scholarly journals Alpha-T: Learning to Traverse over Graphs with An AlphaZero-inspired Self-Play Framework

2021 ◽  
Author(s):  
Qi Wang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimal Path ◽  
Solution Space ◽  
Approximate Solutions ◽  
Small Scale ◽  
Traditional Methods ◽  
Routing Problem ◽  
Monte Carlo Tree Search ◽  
Combinatorial Optimization Problems

Abstract The combinatorial optimization problems on the graph are the core and classic problems in artificial intelligence and operations research. For example, the Vehicle Routing Problem (VRP) and Traveling Salesman Problem (TSP) are not only very interesting NP-hard problems but also have important significance for the actual transportation system. Traditional methods such as heuristics methods, precise algorithms, and solution solvers can already find approximate solutions on small-scale graphs. However, they are helpless for large-scale graphs and other problems with similar structures. Moreover, traditional methods often require artificially designed heuristic functions to assist decision-making. In recent years, more and more work has focused on the application of deep learning and reinforcement learning (RL) to learn heuristics, which allows us to learn the internal structure of the graph end-to-end and find the optimal path under the guidance of heuristic rules, but most of these still need manual assistance, and the RL method used has the problems of low sampling efficiency and small searchable space. In this paper, we propose a novel framework (called Alpha-T) based on AlphaZero, which does not require expert experience or label data but is trained through self-play. We divide the learning into two stages: in the first stage we employ graph attention network (GAT) and GRU to learn node representations and memory history trajectories, and in the second stage we employ Monte Carlo tree search (MCTS) and deep RL to search the solution space and train the model.

scholarly journals Embedding Ordinal Optimization into Tree–Seed Algorithm for Solving the Probabilistic Constrained Simulation Optimization Problems

2018 ◽  
Vol 8 (11) ◽  
pp. 2153 ◽  
Author(s):  
Shih-Cheng Horng ◽  
Shieh-Shing Lin
Keyword(s):  
Optimization Problems ◽  
Simulation Optimization ◽  
Inequality Constraint ◽  
Search Space ◽  
Ordinal Optimization ◽  
Np Hard ◽  
Heuristic Approaches ◽  
Probabilistic Inequality ◽  
Hard Problems ◽  
Np Hard Problems

Probabilistic constrained simulation optimization problems (PCSOP) are concerned with allocating limited resources to achieve a stochastic objective function subject to a probabilistic inequality constraint. The PCSOP are NP-hard problems whose goal is to find optimal solutions using simulation in a large search space. An efficient “Ordinal Optimization (OO)” theory has been utilized to solve NP-hard problems for determining an outstanding solution in a reasonable amount of time. OO theory to solve NP-hard problems is an effective method, but the probabilistic inequality constraint will greatly decrease the effectiveness and efficiency. In this work, a method that embeds ordinal optimization (OO) into tree–seed algorithm (TSA) (OOTSA) is firstly proposed for solving the PCSOP. The OOTSA method consists of three modules: surrogate model, exploration and exploitation. Then, the proposed OOTSA approach is applied to minimize the expected lead time of semi-finished products in a pull-type production system, which is formulated as a PCSOP that comprises a well-defined search space. Test results obtained by the OOTSA are compared with the results obtained by three heuristic approaches. Simulation results demonstrate that the OOTSA method yields an outstanding solution of much higher computing efficiency with much higher quality than three heuristic approaches.

scholarly journals A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhaocai Wang ◽  
Xiaoguang Bao ◽  
Tunhua Wu
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solution Space ◽  
Research Direction ◽  
Chinese Postman Problem ◽  
Combinatorial Optimization Problems ◽  
Chinese Postman ◽  
Dna Algorithm

The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems.

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