{"id":6561,"date":"2019-12-10T23:39:16","date_gmt":"2019-12-10T14:39:16","guid":{"rendered":"http:\/\/www.is.doshisha.ac.jp\/news\/?p=6561"},"modified":"2021-03-09T11:09:16","modified_gmt":"2021-03-09T02:09:16","slug":"%e3%80%90%e9%80%9f%e5%a0%b1%e3%80%91ieee-ssci-2019","status":"publish","type":"post","link":"https:\/\/is.doshisha.ac.jp\/news\/?p=6561","title":{"rendered":"\u3010\u901f\u5831\u3011IEEE SSCI 2019"},"content":{"rendered":"

IEEE SSCI2019\u304cXiamen\u3067\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u3002
\n\u7814\u7a76\u5ba4\u304b\u3089\u306f
\nPerformance Study of Double-Niched Evolutionary Algorithm on Multi-objective Knapsack Problems
\n\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067
\n\u5927\u6fa4(M2)\u304c\u767a\u8868\u3057\u307e\u3057\u305f\u3002
\n
\n\u5b66\u4f1a\u53c2\u52a0\u5831\u544a\u66f8<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n
\u00a0<\/strong>
\n\u5831\u544a\u8005\u6c0f\u540d<\/strong><\/td>\n		 
\n\u5927\u6fa4\u50da\u4e5f<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\n\u591a\u76ee\u7684\u30ca\u30c3\u30d7\u30b6\u30c3\u30af\u554f\u984c\u306b\u95a2\u3059\u308bDouble-Niched Evolutionary Algorithm\u306e\u6027\u80fd\u7814\u7a76<\/td>\n<\/tr>\n
\u767a\u8868\u8ad6\u6587\u82f1\u30bf\u30a4\u30c8\u30eb<\/strong><\/td>\nPerformance Study of Double-Niched Evolutionary Algorithm on Multi-objective Knapsack Problems<\/td>\n<\/tr>\n
\u8457\u8005<\/strong><\/td>\n\u5927\u6fa4\u50da\u4e5f\uff0c\u6e21\u9089\u771f\u4e5f\uff0c\u5ee3\u5b89\u77e5\u4e4b\uff0c\u65e5\u548c\u609f<\/td>\n<\/tr>\n
\u4e3b\u50ac<\/strong><\/td>\nIEEE Computational Intelligence Society<\/td>\n<\/tr>\n
\u8b1b\u6f14\u4f1a\u540d<\/strong><\/td>\nThe 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019)<\/td>\n<\/tr>\n
\u4f1a\u5834<\/strong><\/td>\nSeaview Resort Xiamen<\/td>\n<\/tr>\n
\u958b\u50ac\u65e5\u7a0b<\/strong><\/td>\n2019\/12\/06-2019\/12\/09<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 
\n <\/p>\n

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  1. \u8b1b\u6f14\u4f1a\u306e\u8a73\u7d30<\/li>\n<\/ol>\n

    2019\/12\/06\u304b\u30892019\/12\/09\u306b\u304b\u3051\u3066\uff0c\u4e2d\u56fd\u306eFujian, Xiamen\u306b\u3042\u308bSeaview Resort Xiamen\u306b\u3066\u958b\u50ac\u3055\u308c\u307e\u3057\u305fThe 2019 IEEE Symposium Series on Computational Intelligence\uff08http:\/\/ssci2019.org\/\uff09\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u56fd\u969b\u4f1a\u8b70\u306f\uff0cIEEE Computational Intelligence\u306b\u3088\u3063\u3066\u4e3b\u50ac\u3055\u308c\u305f\u8a08\u7b97\u77e5\u80fd\u306b\u95a2\u3059\u308b\u4e3b\u8981\u306a\u5e74\u6b21\u56fd\u969b\u4f1a\u8b70\u3067\uff0c\u7406\u8ad6\uff0c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u8a2d\u8a08\uff0c\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\uff0c\u304a\u3088\u3073\u95a2\u9023\u3059\u308b\u65b0\u8208\u6280\u8853\u306e\u63a8\u9032\u3092\u3057\u3066\u3044\u307e\u3059\uff0e\u79c1\u306f\u5168\u65e5\u7a0b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u672c\u7814\u7a76\u5ba4\u304b\u3089\u306f\u4ed6\u306b\u5ee3\u5b89\u5148\u751f\u304c\u53c2\u52a0\u3057\u307e\u3057\u305f\uff0e
    \n <\/p>\n

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    1. \u7814\u7a76\u767a\u8868\n
        \n
      • \u767a\u8868\u6982\u8981<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n

        \u79c1\u306f8\u65e5\u306e16\u6642\u304b\u3089\u306e\u30bb\u30c3\u30b7\u30e7\u30f3\u300cIEEE Symposium on Computational Intelligence in Feature Analysis, Selection and Learning in Image and Pattern Recognition (IEEE FASLIP)\u300d\u306b\u53c2\u52a0\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306e\u5f62\u5f0f\u306f\u53e3\u982d\u767a\u8868\u3067\uff0c\u8b1b\u6f14\u6642\u9593\u3068\u8cea\u7591\u5fdc\u7b54\u6642\u9593\u5408\u308f\u305b\u306620\u5206\u3068\u306a\u3063\u3066\u304a\u308a\u307e\u3057\u305f\uff0e
        \n\u4eca\u56de\u306e\u767a\u8868\u306f\uff0c\u300cPerformance Study of Double-Niched Evolutionary Algorithm on Multi-objective Knapsack Problems\u300d\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u767a\u8868\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u4ee5\u4e0b\u306b\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u3092\u8a18\u8f09\u81f4\u3057\u307e\u3059\uff0e<\/p>\n\n\n\n
        Multimodality is often observed in practical optimization problems. Therefore, multi-modal multi-objective evolutionary algorithms (MMEA) have been developed to tackle the multimodality of these problems. However, most of the existing studies focused on population diversity in either an objective or a decision space. A double-niched evolutionary algorithm (DNEA) is a state-of-the-art MMEA that employs a niche-sharing method to improve the population in both the objective and decision spaces. However, its performance has been evaluated solely for real-coded problems and not for binary-coded ones. In this study, the performance of DNEA is evaluated on a multi-objective 0\/1 knapsack problem, and the population diversity in both the objective and decision spaces is evaluated using a pure diversity measure. The experimental results suggest that DNEA is effective for multi-objective 0\/1 knapsack problems to improve the decision space diversity; further, its performance is significantly affected by its control parameter, niche radius.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
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        • \u8cea\u7591\u5fdc\u7b54<\/li>\n<\/ul>\n

          \u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8cea\u7591\u3092\u53d7\u3051\u307e\u3057\u305f\uff0e
          \n 
          \n\u30fb\u8cea\u554f\u5185\u5bb9<\/strong>1<\/strong>
          \nSouthern University of Science and Technology\u306e\u77f3\u6e15\u5148\u751f\u304b\u3089\u306e\u8cea\u554f\u3067\u3059\uff0e\u3053\u3061\u3089\u306e\u8cea\u554f\u306f\u591a\u76ee\u7684\u30ca\u30c3\u30d7\u30b5\u30c3\u30af\u554f\u984c\u3092\u89e3\u3044\u305f\u969b\u306b\u5f97\u3089\u308c\u305f\u89e3\u306e\u9078\u629e\u3055\u308c\u305f\u30d3\u30c3\u30c8\u306e\u6570\u306f\u5927\u4f53\u3069\u306e\u7a0b\u5ea6\u306e\u6570\u304b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u3053\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u79c1\u306e\u56de\u7b54\u306f\uff0c\u6c7a\u5b9a\u5909\u6570\u7a7a\u9593\u306e\u30d3\u30c3\u30c8\u6570\u3092\u76f4\u63a5\u6570\u3048\u3066\u3044\u306a\u304b\u3063\u305f\u306e\u3067\uff0c\u8abf\u67fb\u3057\u3066\u3044\u306a\u3044\u3068\u7b54\u3048\u307e\u3057\u305f\uff0e\u767a\u8868\u7d42\u4e86\u5f8c\u306b\u304a\u8a71\u3092\u4f3a\u3046\u6a5f\u4f1a\u304c\u3042\u308a\uff0c\u5bfe\u8c61\u554f\u984c\u304c\u660e\u78ba\u3067\u3042\u308b\u4eca\u56de\u306e\u30b1\u30fc\u30b9\u306e\u767a\u8868\u306a\u3089\u3070\uff0c\u6c7a\u5b9a\u5909\u6570\u3067\u9078\u629e\u3055\u308c\u308b\u30d3\u30c3\u30c8\u306e\u6570\u3092\u6975\u7aef\u306b\u5236\u9650\u3059\u308b\u3053\u3068\u3067\u5bfe\u8c61\u554f\u984c\u3068\u4f3c\u305f\u3088\u3046\u306a\u6027\u8cea\u3092\u518d\u73fe\u3067\u304d\u308b\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u30a2\u30c9\u30d0\u30a4\u30b9\u3092\u9802\u304d\u307e\u3057\u305f\uff0e
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            \u521d\u3081\u3066\u306e\u56fd\u5916\u3067\u306e\u56fd\u969b\u5b66\u4f1a\u3067\u521d\u306e\u5b66\u4f1a\u306b\u304a\u3051\u308b\u53e3\u982d\u767a\u8868\u3060\u3063\u305f\u305f\u3081\u304b\u306a\u308a\u7dca\u5f35\u3057\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u76f4\u524d\u306e\u30ae\u30ea\u30ae\u30ea\u307e\u3067\u7df4\u7fd2\u3057\u3066\u3044\u305f\u305f\u3081\uff0c\u7121\u4e8b\u81ea\u5206\u306e\u30b9\u30e9\u30a4\u30c9\u306e\u5185\u5bb9\u306b\u3064\u3044\u3066\u306f\u8a71\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff0e\u8cea\u554f\u306b\u304a\u3044\u3066\u306f\uff0c\u82f1\u8a9e\u304c\u308f\u304b\u3089\u305a\uff0c\u8cea\u554f\u8005\u306e\u77f3\u6e15\u5148\u751f\u3084\u30c1\u30a7\u30a2\u3092\u52d9\u3081\u308b\u5ee3\u5b89\u5148\u751f\u304c\u4f55\u5ea6\u304b\u8a00\u3044\u56de\u3057\u3092\u5909\u3048\u3066\u3044\u305f\u3060\u304d\uff0c\u3088\u3046\u3084\u304f\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u304c\uff0c\u81ea\u8eab\u306e\u82f1\u8a9e\u529b\u306e\u4e0d\u8db3\u3092\u75db\u611f\u3044\u305f\u3057\u307e\u3057\u305f\uff0e\u3053\u306e\u5b66\u4f1a\u3092\u901a\u3057\u3066\uff0cIEEE\u306e\u56fd\u969b\u5b66\u4f1a\u306e\u96f0\u56f2\u6c17\uff0c\u9032\u5316\u8a08\u7b97\u624b\u6cd5\u306e\u6d41\u884c\uff0c\u82f1\u8a9e\u80fd\u529b\u306e\u6c42\u3081\u3089\u308c\u308b\u6a5f\u4f1a\uff0c\u4e2d\u56fd\u306e\u9032\u51fa\u306a\u3069\u591a\u304f\u306e\u3053\u3068\u3092\u5b66\u3073\u307e\u3057\u305f\uff0e\u4eca\u5f8c\u306f\u56fd\u5185\u3060\u3051\u3067\u306a\u304f
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            \n <\/p>\n

              \n
            1. \u8074\u8b1b<\/li>\n<\/ol>\n

              \u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e2\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
              \n <\/p>\n\n\n\n
              \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000A New Approach to Generate Solutions Combining Crossover and Estimation of Distribution Operators for EMO Algorithm
              \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Masahide Miyamoto and Shinya Watanabe
              \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a MBEA-Model Based Evolutionary Algorithms
              \nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Abstract\u2014Most of EMO algorithms use crossover operator for generating new solutions. There have been proposed various kinds of crossover in this field and most crossover approaches are good at global optimization but not effective for the problem with strong nonlinearity and dependency.
              \nOn the other hand, Estimation of Distribution Algorithm (EDA) is known as an effective approach without using crossover for generating new solutions. EDA use an estimation of distribution operator for generating new solutions and this operator is known as to be effective for the problem with strong nonlinearity and dependency.
              \nIn this paper, a new approach to generate new solutions combing crossover and estimation of distribution is proposed. The main purpose of this approach is to generate high-quality solutions more effectively by combing each other\u2019s strength. This approach is named as \u201cMOEA\/D Combined with Estimation of Distribution (MOEA\/D-CED)\u201d because this approach is incorporated with MOEA\/D. Through applying to some benchmark problems in this field, the characteristics and effectiveness of MOEA\/D-CED were confirmed by the comparison with original MOEA\/D and MO-CMA-ES.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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              \n <\/p>\n\n\n\n
              \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aA dual-grid dual-phase strategy for constrained multi-objective optimization
              \n\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Mengjun Ming, Rui Wang, Tao Zhang and Hisao Ishibuchi
              \n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aFOCI-2-Foundations of Computational Intelligence NICE-Nature-Inspired Computation in Engineering
              \nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Constrained multi-objective optimization problems (CMOPs) appear frequently in engineering applications. In some case, feasible regions are narrow and\/or disconnected. For this kind of problems, existing constraint-handling methods, integrated with multi-objective evolutionary algorithms, are easily stuck at local optima. Aiming to strengthen the global search ability, a dual-grid dual-push and pull search (DPPS). In the DPPS two populations, corresponding to dual grids, are used individually to explore the feasible and infeasible spaces. Specifically, one population maintains feasible solutions, and the other explores the whole search space without considering constraints. Then the two populations share useful information and pull each other so as to enable the algorithm to search for the optimal feasible region (i.e., Pareto solution set). To demonstrate the effectiveness of the proposed algorithm, the MOEA\/D integrated DPPS(MOEA\/D-DPPS) is tested on a frequently-used benchmark suite as well as a newly-constructed suite. Experimental results clearly show the superiority of MOEA\/D-DPPS compared with sic state-of-the-art algorithms.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

              \u3053\u306e\u767a\u8868\u306fSouthern University of Science and Technology\u306e\u77f3\u6e15\u5148\u751f\u306b\u3088\u308b\u767a\u8868\u3067\uff0c\u5236\u7d04\u6761\u4ef6\u4ed8\u304d\u306e\u591a\u76ee\u7684\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u65b0\u305f\u306a\u624b\u6cd5\u3092\u63d0\u6848\u3059\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u6bcd\u96c6\u56e3\u30922\u5206\u5272\u3057\uff0c\u4e00\u65b9\u306f\u5236\u7d04\u6761\u4ef6\u306b\u5f93\u3063\u3066\u63a2\u7d22\u3092\u884c\u3044\uff0c\u3082\u3046\u4e00\u65b9\u306f\uff0c\u5236\u7d04\u6761\u4ef6\u306b\u5f93\u308f\u305a\u306b\u63a2\u7d22\u3092\u884c\u3046Push Stage\u3068\u3069\u3061\u3089\u306e\u6bcd\u96c6\u56e3\u3082\u5236\u7d04\u6761\u4ef6\u306b\u5f93\u3046\u3088\u3046\u306b\u306a\u308bPull Stage\u3092\u5207\u308a\u66ff\u3048\u308b\u3082\u306e\u3067\u3057\u305f\uff0e\u79c1\u3082\u4ee5\u524d\u3053\u306e\u3088\u3046\u306b\u624b\u6cd5\u3092\u5207\u308a\u66ff\u3048\u308b\u3088\u3046\u306a\u3053\u3068\u3092\u3084\u308d\u3046\u3068\u3057\u3066\u3044\u305f\u306e\u3067\u53c2\u8003\u306b\u306a\u308a\u307e\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u624b\u6cd5\u3092\u5207\u308a\u66ff\u3048\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u5207\u308a\u66ff\u3048\u308b\u6761\u4ef6\u3068\u3044\u3046\u30d1\u30e9\u30e1\u30fc\u30bf\u304c\u5897\u3048\u3066\u3057\u307e\u3046\u306e\u3067\uff0c\u3053\u306e\u767a\u8868\u306e\u8ab2\u984c\u3067\u3082\u8ff0\u3079\u3089\u308c\u3066\u3044\u305f\u901a\u308a\uff0cpush stage\u3067\u53ce\u675f\u3057\u3059\u304e\u3066\u3057\u307e\u3063\u3066\uff0c\uff0c\u601d\u3046\u3088\u3046\u306b\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u51fa\u306a\u3044\u3068\u3044\u3046\u3053\u3068\u3092\u4eca\u5f8c
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              \n\u53c2\u8003\u6587\u732e<\/p>\n