Classical Multi-objective Algorithms Research Articles

A Scheduler for Smart Home Appliances Based on a Novel Concept of Tariff Space.

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day from official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time-shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method referred to as tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (hierarchical) available in the language portfolio in which the project has been executed (the Julia language). As a result, while both methodologies yield similar results, the approach presented in this article demonstrates a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.

Ensemble artificial bee colony algorithm with Q-learning for scheduling Bi-objective disassembly line

This study addresses a bi-objective disassembly line scheduling problem (Bi-DLSP), considering interference relationships among tasks. The objectives are to optimize the total disassembly time and the smoothing index simultaneously. First, we propose a mathematical model for the concerned problems. Second, improved artificial bee colony (ABC) algorithms are developed to solve the Bi-DLSP, and seven different local search operators are created to strengthen the performance of the ABC algorithms. Third, to further enhance the improved ABC algorithms, we design two Q-learning-based strategies for selecting high-quality local search operators and integrate them into the ABC algorithm during iterations. Finally, we evaluate the effectiveness of the proposed strategies by comparing the classical ABC algorithm, its variants, and two classical multi-objective algorithms for solving 21 instances. We validate the proposed model using the Gurobi solver and compare its results and time efficiency with the proposed algorithms. The experimental results show that the proposed ABC algorithm based on Q-learning (ABC_QL1) performs the best in solving related problems. This study provides a new approach for solving the Bi-DLSP and demonstrates the effectiveness and competitiveness of our method, providing useful insights for research and applications in related fields.

Two sided disassembly line balancing problem with rest time of works: A constraint programming model and an improved NSGA II algorithm

In the process of disassembly, it is difficult for workers who have been in working condition for a long time to ensure the high-quality completion of the tasks they undertake, which will lead to the reduction of disassembly profit. Providing workers with longer rest time within a certain range can improve the quality of work and thus increase dismantling profits. In response to the above phenomenon, this study proposes for the first time a two sided disassembly line balancing problem with rest time of works (TDLBP-RTW), aiming to optimize the disassembly profit, number of workstations, number of sub workstations, workload smoothness, and hazard index. In addition, the existing algorithms for solving the disassembly line balance problem often do not consider the distribution of feasible solutions. Their search process for the solution space tree is quite random, leading to serious repeated searches and resulting in a significant waste of search resources. To solve the above problems, this study improves the NSGAII algorithm and designs a multi-objective evolutionary algorithm (NSGAII-FSDRSA) considering feasible solution distribution and real-time search resource allocation. The biggest difference between NSGAII-FSDRSA and traditional methods is that NSGAII-FSDRSA can analyze the distribution of feasible solutions and the allocation of search resources, and allocate more search resources to areas with dense feasible solution distribution and less search resource allocation based on the analysis results. The performance of the proposed algorithm is verified by solving three classical examples and by comparing with many meta-heuristic algorithms. Then apply the proposed model and method to the refrigerator disassembly production line of a disassembly enterprise in China. The disassembly scheme of NSGAⅡ-FSDRSA is superior to five classic multi-objective algorithms. The results indicate that this method can improve the performance of the disassembly line.

Evaluation on Software Performance Optimization Based on Multi-objective Optimization Algorithms

The tasks undertaken by software in various tasks include precise positioning of computational targets, utilization of hardware resources, and so on. Therefore, updating software performance is an essential and important step. However, currently, software iteration in computers is not smooth sailing. Therefore, this article aimed to study the problem of software performance optimization through the direction of objective optimization algorithms. This article used the method of evaluating parameter optimization results to conclude that multi-objective optimization algorithms have stronger computational power in models in other fields. The optimization results of the target software are statistically analyzed through web log mining, and the Chebyshev method is also used to decompose one of the classic multi-objective algorithms, MOEA/D. Finally, the optimization approach was divided into two types: multi-objective optimization algorithm and single objective optimization algorithm. Two other computers with identical configurations were selected to use these two algorithms for experiments. The multi-objective optimization algorithm was set as the experimental group, while the single objective optimization algorithm was set as the control group. After performing several software optimization operations, the results showed that the software performance optimization operation based on the multi-objective optimization algorithm improved the software running speed much higher than the control group executing the single objective optimization algorithm. The final conclusion is that in the research of software performance optimization, MOEA/D in multi-objective optimization algorithms not only defeats single objective optimization algorithms, but also is a highly efficient means in itself.

Knowledge-Based Evolutionary Optimizing Makespan and Cost for Cloud Workflows

Workflow scheduling is essential to simultaneously optimize the makespan and economic cost for cloud services and has attracted intensive interest. Most of the existing multi-objective cloud workflow scheduling algorithms regard the focused problems as black-boxes and design evolutionary operators to perform random searches, which are inefficient in dealing with the elasticity and heterogeneity of cloud resources as well as complex workflow structures. This study explores the characteristics of cloud resources and workflow structures to design a knowledge-based evolutionary optimization operator, named KEOO, with two novel features. First, we develop a task consolidation mechanism to reduce the number of cloud resources used, reducing the economic cost of workflow execution without delaying its finish time. Then, we develop a critical task adjustment mechanism to selectively move the critical predecessors of some tasks to the same resources to eliminate the data transmission overhead between them, striving to improve the economic cost and finish time simultaneously. At last, we embed the proposed KEOO into four classical multi-objective algorithms, i.e., NSGA-II, HypE, MOEA/D, and RVEA, forming four variants: KEOO-NSGA-II, KEOO-HypE, KEOO-MOEA/D, and KEOO-RVEA, for comparative experiments. The comparison results demonstrate the effectiveness of the KEOO in improving these four algorithms in solving cloud workflow scheduling problems.

Radial basis network simulation for noisy multiobjective optimization considering evolution control

Although some studies have achieved good optimization results when solving noisy multiobjective optimization problems (NMOPs), these studies require considerable computational resources to reduce the impact of noise on evolutionary algorithms. Inspired by the universal approximation theorem, we use a radial basis network (RBN) to estimate the noise-free fitness and embed it into the classical multiobjective algorithm NSGA-II to solve NMOPs, called RBN-NSGA-II. Specifically, the averaged fitness after individual resampling is used as the training sample of the RBN, and then, the trained RBN is used to simulate the fitness functions and estimate the noise-free fitness. Also, to better simulate the shape of the fitness functions, the RBN is adaptively updated based on its estimated error, and its training samples are gradually increased based on the population distribution in the search space. Experimental results demonstrate that RBN-NSGA-II markedly improves the optimization speed while maintaining good search ability compared with six state-of-the-art algorithms, which is important in practical noisy optimization scenarios.

A genetic simulated annealing algorithm for parallel partial disassembly line balancing problem

The timely recovery and disassembly of waste electrical and electronic equipment (WEEE) can not only obtain a higher economic benefit but also can reduce the impact of hazardous substances on the environment. The parallel disassembly line can disassemble different kinds of WEEE synchronously and improve disassembly efficiency. Therefore, a parallel partial disassembly line balancing model with stochastic disassembly time is established in this paper. The evaluation indexes of the disassembly line include the number of workstations, workload smoothness, and disassembly profits. A new genetic simulated annealing algorithm is proposed to optimize the model. The encoding and decoding strategies are constructed according to the characteristics of partial disassembly and parallel layout. Two-point mapping crossover and single-point insertion mutation operations are designed to ensure that the disassembly sequence meets the precedence constraints and disassembly constraints. The simulated annealing operation is applied to the results of the genetic operation. The proposed algorithm obtains better solutions than the tabu search algorithm in stochastic parallel assembly line balancing problems, and the proposed algorithm has better performance than the CPLEX solver, genetic algorithm, and simulated annealing in parallel disassembly line balancing problems. Finally, a parallel partial disassembly line for waste televisions and refrigerators is constructed, and the performance of the proposed multi-objective algorithm is superior to those of five classical multi-objective algorithms. The results show that the proposed model has a better practical application ability and that the proposed algorithm can improve the performance of disassembly lines.

Sustainable smart waste classification and collection system: A bi-objective modeling and optimization approach

In the context of the “smart city”, information and communication technologies (ICT) have become indispensable in the planning and design of modern municipal solid waste management. Due to more waste varieties, there are urgent calls to implement waste classification worldwide, which promotes resource recycling to achieve sustainable development. In this paper, we present an ICT-based smart waste classification and collection system (SWCCS) that is abstracted as a bi-objective mathematical programming model to optimize the waste collection problem. To implement the proposed SWCCS effectively, we design a novel multi-objective hybrid algorithm based on the whale optimization and genetic algorithms (MOGWOA) with an improved convergence factor and a fast, non-dominated sorting method. A comparison of our algorithm with two classical multi-objective algorithms on generated test instances and on a real-world case shows that the proposed MOGWOA is more effective for optimizing the established model. This paper demonstrates how the ICT-based SWCCS works and how it can help sanitation companies improve waste collection both economically and environmentally.

A multi-objective algorithm for U-shaped disassembly line balancing with partial destructive mode

The disassembly line is the best way to deal with large-scale waste electrical and electronic equipment. Balancing of disassembly line is a hot and challenging problem in recent years. Given the uncertainty factors including corrosion and deformation of parts and components of waste products, this paper introduces the destructive mode and uncertainty disassembly time into the disassembly line and establishes a multi-objective disassembly line balancing model, considering partial destructive mode and U-shaped layout. The model aims to reduce the number of stations, balance the workload and reduce energy consumption while increasing the disassembly profit. A new multi-objective discrete flower pollination algorithm is proposed to solve the problem. Both task assignment and disassembly modes are considered in the encoding and decoding strategies of the flowers. Combining the discrete characteristics of the problem, the cross-pollination and self-pollination behaviors of the algorithm are redefined. The performance of the proposed algorithm is verified by solving two classical examples and by comparing with seven meta-heuristic algorithms. Then the proposed model and method are applied to a television disassembly line of a disassembly enterprise in China. The disassembly schemes of the proposed algorithm are superior to that of the five classical multi-objective algorithms. The results show that the proposed method can improve the performance of the disassembly line.

Multi-Objective Neighborhood Search Algorithm Based on Decomposition for Multi-Objective Minimum Weighted Vertex Cover Problem

The multi-objective minimum weighted vertex cover problem aims to minimize the sum of different single type weights simultaneously. In this paper, we focus on the bi-objective minimum weighted vertex cover and propose a multi-objective algorithm integrating iterated neighborhood search with decomposition technique to solve this problem. Initially, we adopt the decomposition method to divide the multi-objective problem into several scalar optimization sub-problems. Meanwhile, to find more possible optimal solutions, we design a mixed score function according to the problem feature, which is applied in initializing procedure and neighborhood search. During the neighborhood search, three operators ( A d d , D e l e t e , S w a p ) explore the search space effectively. We performed numerical experiments on many instances, and the results show the effectiveness of our new algorithm (combining decomposition and neighborhood search with mixed score) on several experimental metrics. We compared our experimental results with the classical multi-objective algorithm non-dominated sorting genetic algorithm II. It was obviously shown that our algorithm can provide much better results than the comparative algorithm considering the different metrics.

A bi-objective study of the minimum latency problem

We study a bi-objective problem called the Minimum Latency-Distance Problem (mldp) that aims to minimise travel time and latency of a single-vehicle tour designed to serve a set of client requests. This tour is a Hamiltonian cycle for which we aim to simultaneously minimise the total travel time of the vehicle and the total waiting time (i.e., latency) of the clients along the tour. This problem is relevant in contexts where both client satisfaction and company profit are prioritise. We propose two heuristic methods for approximating Pareto fronts for mldp: SMSA that is based on a classic multi-objective algorithm and EiLS that is based on a novel evolutionary algorithm with intelligent local search. We report computational experiments on a set of artificially generated problem instances using an exact method and the two proposed heuristics, comparing the obtained fronts in terms of various quality metrics.

Multiline Distance Minimization: A Visualized Many-Objective Test Problem Suite

Studying the search behavior of evolutionary many-objective optimization is an important, but challenging issue. Existing studies rely mainly on the use of performance indicators which, however, not only encounter increasing difficulties with the number of objectives, but also fail to provide the visual information of the evolutionary search. In this paper, we propose a class of scalable test problems, called multiline distance minimization problem (ML-DMP), which are used to visually examine the behavior of many-objective search. Two key characteristics of the ML-DMP problem are: 1) its Pareto optimal solutions lie in a regular polygon in the 2-D decision space and 2) these solutions are similar (in the sense of Euclidean geometry) to their images in the high-dimensional objective space. This allows a straightforward understanding of the distribution of the objective vector set (e.g., its uniformity and coverage over the Pareto front) via observing the solution set in the 2-D decision space. Fifteen well-established algorithms have been investigated on three types of ten ML-DMP problem instances. Weakness has been revealed across classic multiobjective algorithms (such as Pareto-based, decomposition-based, and indicator-based algorithms) and even state-of-the-art algorithms designed especially for many-objective optimization. This, together with some interesting observations from the experimental studies, suggests that the proposed ML-DMP may also be used as a benchmark function to challenge the search ability of optimization algorithms.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.