Multi-objective Immune Genetic Algorithm Research Articles

Techno-economic and environmental benefits-oriented human–robot collaborative disassembly line balancing optimization in remanufacturing

The remanufacturing process, driven by human–robot collaboration technology, is becoming an important carrier for the circular economy, contributing to economic development while significantly reducing environmental pressure. However, existing researches on human–robot collaboration disassembly line have certain limitations and fails to address economic and environmental considerations adequately. Therefore, in this study, a techno-economic and environmental benefit-oriented human–robot collaborative disassembly line balancing problem (TEBHRC-DLBP) was formulated that requires the utilization of human and robot resources to improve disassembly efficiency and quality and to facilitate decision-making on recycling options for disassembled parts to maximize economic and environmental benefits. First, a mixed-integer programming (MIP) model was developed for the TEBHRC-DLBP to minimize the number of workstations, minimize the smoothing index, and maximize techno-economic and environmental benefits. Second, a multi-objective immune genetic algorithm (MIGA) was developed to solve the proposed TEBHRC-DLBP efficiently. A five-layer encoding method and a triple decoding strategy were constructed based on the problem characteristics, and immune operators were introduced into the well-known NSGA-II structure to interfere with the global search process with a certain degree of strength to avoid invalid work and to improve algorithm efficiency. In addition, the correctness and validity of the proposed MIP model and the MIGA were verified using the case of a small-scale personal computer. In 21 benchmark tests with scales ranging from 7 to 148, the proposed MIGA achieved significantly better results than the seven algorithms reported in the literature and obtained the best value for 16 benchmarks. Finally, the application of the MIGA to the disassembly case of a power battery module demonstrates its good stability, convergence, and diversity, as well as its excellent practical application ability. In particular, the proposed method can change the disassembly scheme according to different disassembly planning periods to maximize economic and environmental benefits.

Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

Retraction Note to: A fuzzy multi-objective immune genetic algorithm for the strategic location planning problem

Retraction Note to: A fuzzy multi-objective immune genetic algorithm for the strategic location planning problem

RETRACTED ARTICLE: A fuzzy multi-objective immune genetic algorithm for the strategic location planning problem

The distribution center (DC) location problem is one of the most important decisions for logistics systems. Owing to vague concept frequently represented in decision data, some new multiple criteria based decision-making model is proposed to solve the DC location problem with significant time-effectiveness under fuzzy environment. In the proposed model, we consider time penalty cost, operation cost, damage cost, infrastructure, market conditions, government policy, technical conditions and land resources from a comprehensive view. Meanwhile, trapezoidal fuzzy numbers are used to describe the delivery time window and the service level. Then, intuitionistic fuzzy sets and immune genetic algorithm are utilized to solve the model. Finally, a numerical example is presented to illustrate the effectiveness of the proposed approach.

Multi-objective immune genetic algorithm solving nonlinear interval-valued programming

This work studies one multi-objective immune genetic algorithm with small population to solve a general kind of unconstrained multi-objective interval-valued programming. In this optimization approach, those competitive individuals are discriminated based on interval arithmetic rules and a possibility model; a crowding degree model in interval-valued environments is developed to eliminate redundant individuals; the current population promotes different individuals to evolve towards specific directions by population sorting and immune evolution, while those elitist individuals found accelerate to explore the desired regions through genetic evolution. The theoretical analysis has showed that the computational complexity of the proposed approach depends mainly on the elitist population size. Comparative experiments have illustrated that the approach can take a rational tradeoff between effect and efficiency. It can perform well over the compared approaches as a whole, and has the potential to solving multi-modal and hard multi-objective interval-valued programming problems.

Uplink Scheduling of Navigation Constellation Based on Immune Genetic Algorithm.

The uplink of navigation data as satellite ephemeris is a complex satellite range scheduling problem. Large–scale optimal problems cannot be tackled using traditional heuristic methods, and the efficiency of standard genetic algorithm is unsatisfactory. We propose a multi-objective immune genetic algorithm (IGA) for uplink scheduling of navigation constellation. The method focuses on balance traffic and maximum task objects based on satellite-ground index encoding method, individual diversity evaluation and memory library. Numerical results show that the multi–hierarchical encoding method can improve the computation efficiency, the fuzzy deviation toleration method can speed up convergence, and the method can achieve the balance target with a negligible loss in task number (approximately 2.98%). The proposed algorithm is a general method and thus can be used in similar problems.

A Multi-Objective Immune Genetic Algorithm for Project Scheduling on Multi-Skill Resources

In this paper, we address the project scheduling problem with the aim of making the best use of people's talents while minimizing project makespan and the amount of wasted resources. The purpose of proposing this problem is to assist project managers to improve the quality of products and save cost. To solve this problem, we also proposed an immune genetic algorithm (IGA). This algorithm designs feasible schedule for projects. By designing computational experiments carried out on j60 from PSPLIB, we evaluate the performance of proposed IGA as well as compare it with traditional GA. It turns out that proposed IGA performs much better in the aspect of improving diversity and minimizing makespan, which provides more diverse and effective solutions.

Multi-Objective Immune Genetic Algorithm and its Application in the Optimal Operation of Hydro Power Stations

The cascade development of river basin has important implication on the society, economy and ecological environment. To carry out optimal control and operation of hydro power stations can obtain a more significant overall efficiency, and provide basic support and guarantee to energy conservation and construction of ecological civilization. Taking into account the complexity and multi-objective property of operation of hydro power stations, as well as increasingly severe eco-environmental problems, this paper presents an optimal operation model of hydro power stations considering ecological environment water demand, and proposes an improved multi-objective immune genetic algorithm (MOIGA) to solve the model for better control of hydro power stations. This model is applied to the Xinan-Fuchun hydro power stations, and result shows that MOIGA is feasible for the control and optimal operation of hydro power stations.

Study on Improved Multi-Objective Adaptive Artificial Immune Genetic Algorithm

In this paper, the traditional NSGA model in solving multi-objective optimization problem exists the computational complexity, lack of elitism and the need to set shared radius etc. defects. we use the advantage of the artificial immune system, such as good generalization, self-organization and so on, propose a improved multi-objective adaptive artificial immune genetic algorithm that by the use of fast non-dominated sorting and crowding distance, reduces the algorithm complexity and improves its stability and versatility; utilizes the immune memory cells to optimize the population quality, accelerate the antibody reaction speed and raise the optimize efficiency; updates the populations by the non-inferior rank and crowding distance to improve the algorithm function of the search. This paper elaborates the algorithm steps in detail and verifies this algorithm. Through the three different dimensions of test functions, the simulation results show that this algorithm is effective and feasible.

Application of the Multi-objective Immune Genetic Algorithm in the Operation of Cascade Reservoirs

To improve the application effect of genetic algorithm in multi-objective optimization operation of cascade reservoirs,a new algorithm is developed by introducing the immune mechanism in standard genetic algorithm.Meanwhile,this multi-objective immune genetic algorithm was employed in the optimization operation of Zhougongzhai-Jiaokou cascade reservoirs.The results showed that the multi-objective immune genetic algorithm could overcome the disadvantages of standard genetic algorithm,such as low convergence speed,easily trapping into the local extremum and premature.