A Novel Immune Clonal Algorithm for MO Problems

Shang, Ronghua; Jiao, Licheng; Liu, Fang; Ma, Wenping

doi:10.1109/tevc.2010.2046328

Cited by 134 publications

(62 citation statements)

References 42 publications

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“…Experiments have demonstrated that among the existing algorithms, the quality of non-dominated solution sets obtained using the NICA is, in most cases, far superior to that obtained using other algorithms [32].Therefore, the present study uses the NICA as an essential method for solving the multi-objective LUA optimization problems. Figure 2 shows the flow chart of the NICA algorithm.…”

Section: Design Of the Multi-objective Optimization Algorithmmentioning

confidence: 95%

“…In the NICA, some calculation processes, such as cloning, non-dominated sorting, and crowding distance calculation, are unrelated to the antibody data structure and are conducted by directly applying the classical model [32]. Due to the fact that antibody mutation is closely related to the antibody data structure and also to avoid the generation of solutions that violate the constraint conditions during the random mutation process, it is necessary to redesign the initialization and mutation algorithms in the NICA.…”

Section: Design Of the Multi-objective Optimization Algorithmmentioning

confidence: 99%

“…However, in many application scenarios, it is difficult to reasonably determine the weights of the LUA optimization objectives; (b) Most of the multi-objective optimization models for LUA or land use spatial allocation are based on genetic algorithms or improved genetic algorithms, mainly because of their ability to find a wide spread of Pareto-optimal solutions. However, in some cases, previous studies have demonstrated some shortcomings of these algorithms, such as the ability to converge to Pareto front, diversity of obtained non-dominated solutions, and the running time [31,32]. Therefore, it is necessary to design a multi-objective optimization model for LUA based on some other more effective multi-objective optimization algorithms.…”

mentioning

confidence: 99%

“…AIS models have shown their advantages in the multi-objective optimization field. The results of the existing studies demonstrate that the AIS models perform exceptionally well in solving high-dimensional, non-linear, and complex problems, and they have become the focus of the intelligent optimization field [32].…”

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confidence: 99%

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Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

Xiang

2015

Sustainability

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Abstract:As the main feature of land use planning, land use allocation (LUA) optimization is an important means of creating a balance between the land-use supply and demand in a region and promoting the sustainable utilization of land resources. In essence, LUA optimization is a multi-objective optimization problem under the land use supply and demand constraints in a region. In order to obtain a better sustainable multi-objective LUA optimization solution, the present study proposes a LUA model based on the multi-objective artificial immune optimization algorithm (MOAIM-LUA model). The main achievements of the present study are as follows: (a) the land-use supply and demand factors are analyzed and the constraint conditions of LUA optimization problems are constructed based on the analysis framework of the balance between the land use supply and demand; (b) the optimization objectives of LUA optimization problems are defined and modeled using ecosystem service value theory and land rent and price theory; and (c) a multi-objective optimization algorithm is designed for solving multi-objective LUA optimization problems based on the novel immune clonal algorithm (NICA). On the basis of the aforementioned achievements, MOAIM-LUA was applied to a real case study of land-use planning in Anlu County, China. Compared to the current land use situation in Anlu County, optimized LUA solutions offer improvements in the social and ecological objective areas. Compared to the existing models, such as the non-dominated sorting genetic algorithm-II, experimental results demonstrate that the model designed in the present study can obtain better non-dominated solution sets and is superior in terms of algorithm stability.

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Section: Design Of the Multi-objective Optimization Algorithmmentioning

confidence: 95%

Section: Design Of the Multi-objective Optimization Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

Xiang

2015

Sustainability

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“…Among various multi-objective optimization algorithms, multi-objective evolutionary algorithms (MOEAs), the most significant ones are multiobjective genetic algorithms (GA) (Deb et al 2002, Mohapatra et al 2015), multi-objective particle swarm optimization algorithms (MPSO) (Kotinis 2014;Zhang et al 2013;Niknam et al 2011;Tsaia et al 2010), multi-objective evolutionary algorithms (Shin et al 2011;Zhu et al 2014;Tana et al 2014), multi objective immune clone algorithms (Shang et al 2012), group search optimizer (Wang et al 2012), multi-objective frog leaping algorithms (Taher et al 2010;Safaei Arshia et al 2014) and so on. However, hybrid algorithms have received considerable attention recently.…”

Section: Multi-objective Optimization Problem In Scmentioning

confidence: 99%

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

et al. 2017

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There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multiproduct multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.

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A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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A Novel Immune Clonal Algorithm for MO Problems

Cited by 134 publications

References 42 publications

Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

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