目の検討
著者
衛 ?, 永松 哲郎
雑誌名
鹿児島大学水産学部紀要=Memoirs of Faculty of
Fisheries Kagoshima University
巻
57
ページ
1-10
別言語のタイトル
Study on Principal Particulars of a Fishing
Vessel with the Maximum Fishery Profit by
Genetic Algorithms
Mem. Fac. Fish. Kagoshima Univ., Vol. 57, pp. 1~10 (2008)
2 ോ ఱ ڠ କ ॲ ڠ ໐ ݽ ު ࢥ ڠ (Fisheries Engineering, Faculty of Fisheries, Kagoshima University, 61-31 Shimoarata 5, Kagoshima, 9:1-1167 Japan) ȁ
Corresponding author, E-mail: nagamatu@fi sh.kagoshima-u.ac.jp
֒ഥഎͺσΌςΒθͥ͢ͅݽႻ၌ףडఱݽ͈৽ါ࿒͈൦
߃ාȄݽު࡙͈ࡘઁȄݽث͈೩ྸȄକॲවၾ ͈௩ఱȄݽݽުਲম৪͈ࣞႢا͂ࢃࠑ৪ະ௷Ȅయ࠺ ௮ၾ͈ࠣࡘȄݽݽުࠐאఘ͈ञྩ಼ًȄࢵͅීၳثڒ ͈ࣞ൯̈́̓Ȅུ͈ݽݽުͬ৾ͤے̩۪ޏ͉ࡕ̱̯ͬ ௩̱̞̀ͥȃ̭͈̠̈́͢ેޙ͈ئ́Ȅြུ͈͈ݽ ݽު̧͈̜͓ܿͥউͬࡉ̳দ͙̦࣐̞ͩͦ̀ͥ2ȫ3ȫ ȃ ࣽࢃȄݽݽު̧̦ॼ̹͉ͥ͛ͅഐ୨̥̾ࡕڒ̈́କॲ ࡙ۯၑ͈ئ́Ȅनॳ͈̞ࣞݽ͈ٳอ̦ݥ͛ͣͦ̀ ̞ͥ4ȫȃ̷͈̹͉͛ͅȄݽ͈જΥ͞જႁاȄ೩Α ΠاȄݽڕ͈ৗ͞ഽ༗͈ࣞഽاȄհ͞ݳਯ ͈̞ࣞݽ͞ݽႻ౾͈ٳอȄී࢜ષ͈̈́̓ڟ૧എ ̈́ܿٳอ̦ຈါ̜̠́ͧȃ ̭͂ͧ́Ȅड߃ཤ؎ݽུ͈͂ݽ͓ͬͥ͂Ȅ ߿̦ఱ̧̩֑̠̭̦͂ঐഊ̯ͦ̀Ȅུ߿ݽ͈ࡉೄ̱ ̧͈̥̫̞̽̈́̽̀ͥͅ5ȫ6ȫ ȃ଼ 25 ා 9 ͅ൲ႁ͈ ෝܖ͈ࡔ௱গȄঐݽު͈ݺخ̤̫ͥͅݽ͈Π ϋତٴߊ͈ࡉೄ̱൝̦࣐ͩͦ̀Ḙ͉֑̏ͦ́͂̽͘ ̹૧̱̞ݽ͈߿ٳอ͈ܥ̦ࣞͤ͘Ȅनॳ͈̞ࣞ ߿̦ݥ̠̹͛ͣͦͥ̈́̽͢ͅȃ؎ਗ͈ݽ͈̠̈́͢ࢩ ̞ࢿโ࿂ୟ͞ܥ٫ا͈ଔૺͤ͢ͅજΥσΆȜȆજ૽Ȇ જႁاȄ͍ͅैު۪ޏ͈٨Ȇ٨ၻ̦خෝ̥͂̈́ͥ͜ ̱̞ͦ̈́ȃ ̭͈̠͢ͅḘ̥͈̏ͦͣݽ͉̭͈ͦ́͘ݽ͉͂ ֑̹̽૧̱̞߿͈࡛̦ݥ̦͛ͣͦͥȄݽݽު͈ ࠲̈́ͥࠐא࣐̠̹͉͈̠ͬ͛̓̈́͢ͅ߿̧̳͓̥͂ দ࣐॒ࢋ̯̞ͦ̀ͥȃ̭͉ͦ́͘ఘࢯ̦ड͂̈́ͥ AbstractGenetic algorithm GA is widely applied to the non-linear optimum problems in the various technological fi elds. Fishing vessels are faced to serious problems such as low benefi ts, low fi sh prices, lack of young fi shermen and so on. In the present study, the principal particulars of the optimum fi shing vessel which earns the highest benefi t in a year are studied by using the genetic algorithm. At fi rst, two GA methods, bit-string GA and real coded GA, are examined their applicability to the present problem. The results indicate that GA is a useful tool for study on the principal particulars of the fi shing vessel at the initial design stage and there is not much to choose between two GA methods. The optimum ship form is dependent on the ship speed and its block coeffi cient becomes smaller with increase of her speed. A promising solution for the problem of high fuel price is shown to adopt low ship speed and large gross tonnage.
מȁὬȄ
2ȁזઐȁഓ
2ɖStudy on Principal Particulars of a Fishing Vessel with the Maximum Fishery Profi t
by Genetic Algorithms
Qi Wei,
1Tetsuo Nagamatsu
1*Key words : Bit-string genetic algorithm, Real coded genetic algorithm, Ship resistance, Maximum gross profi t,
߿Ȅ̜̞͉ͥજΥͬݞ̱̹߿͈ࡄݪ̦৽࣐ͩͅ ̞ͦ̀ͥȃ̷ུ̭́ࡄݪ͉́Ḙ̥͈̏ͦͣݽݽުࠐא ͅݥ͛ͣͦͥड͜ਹါ̈́नॳͅచ̱̀डഐ̈́߿ͬࠗ ْ̳ͥडഐا༹̞̾̀ͅ൦̱̹ȃडഐا༹͈͌͂ ̱̾͂̀Ȅड߃ಕ࿒̯̞͈̦ͦ̀ͥ֒ഥഎͺσΌςΒθ ȪGAȇgenetic algorithmȫ̜́ͥ7ȫ ȃ̭͉ͦਅ͈ૺا ًͬ૯যͥࠁ́ତၑഎ࿚ఴ๊͈֚͒ഐဥͬփ̱̀ਬ ౬ͬଲయྀͅಈষ٨ၻ̳ٜͥଢ଼༹̜́ͥȃ༹̭͈༷͉ຈ ̴૯͈डഐٜͬဓ̢̞̠ͥ͂༗બ͉̞̦̈́Ȅࠗॳ͈ܢ ૄ͈୭ͅඅ༆̈́Φ;Χ;͉ຈါ̩́̈́Ȅໝॠ̈́࿒എ ۾ତͅచ̱̀͜ယօͅडഐͅ߃̞ٜͬං̧̭̦ͥ͂́ ͥȄ̞̠͂၌ത̦̜ͥȃ̹͘Ȅఈ͈डഐا༹͓̀ͅȄ डഐٜͬං͈ͥ́ࠗ͘ॳশۼ͈ౣੀ̦ࡉࣺͦ͘Ȅౝً॑ ͈ఉအͤ͢ͅޫਫ਼डഐٜͅۿ̩̞̭ͤ͂͜ͅݷ̬ͣ ̞ͦ̀ͥ9ȫȃུࡄݪ͉́ȄාۼݽႻ၌ף̦डఱ͂̈́ͥݽ ߿͈৽ါ࿒̞̾̀ͅȄ֒ഥഎͺσΌςΒθͬဥ̞̀ ൦̱̹͈̜́ͥ͜ȃ֒ഥഎͺσΌςΒθ̱͂̀ȄΫΛ ΠΑΠςϋΈ GA ͂ତ GA ͈ 3 ༹͈༷̾ͬဥ̞̹ȃ ֒ഥഎͺσΌςΒθ ֒ഥഎͺσΌςΒθ8ȫ ͉́Ȅఉତ͈ࡢఘ̥ͣ̈́ͥਬ౬ ͈ಎ̥ͣ͂̈́ͥࡢఘͬໝତ఼̱Ȅ͈ए̽̀͢ͅ ͈අȪ௺ȫ̧ͬ֨ࠑ̞̺ঊ̲ͬͥȃ఼͈͉Ȅ ࿒എ۾ତ͈Ȫഐࣣഽȫ̦ఱ̧̞ࡢఘ̦̞ࣞږၚ఼́ ̯ͦͥȃ൳̲ࡢఘ̦̱͂̀ໝତ఼̯̭̜ٝͦͥ͂͜ ͥȃ̷͈ࠫضȂഐࣣഽ͈̞ࣞ௺ͬ̾ࡢఘ̦ঊః̱͂ ̧̀ॼͤȂ௩̢̞̩̭̀͂̈́ͥͅȃ ΫΛΠΑΠςϋΈ GA ͉́Ȅ๊͈֚֚̾ͅࡢఘ͉֚̾ ͈அఘ́ນ̯ͦȄஅఘ͉ఉତ͈֒ഥঊ́ࢹ଼̯ͦ̀ ̞̀Ȅڎ֒ഥঊ͉Ȫ1,2ȫ͈ 3 ૺତ́ນ̯ͦͥȃ̳̻̈́ͩȄ அఘȪࡢఘȫ͉Ȫ1,2ȫ͈֒ഥঊႥȪΫΛΠΑΠςϋΈȫ ́ນ̯ͦͥȃ႕̢͊Ȅ୭ࠗ་ତ x ͬ 6 ͈ࠥ֒ഥঊ́ນ̳ ̳͂ͥ͂Ȅ43 ࡢ͈ၗ८എ̈́ତ̦ x ̱͂̀৾ͤංͥํ ս̞̠̭͂͂̈́ͥͅȃडഐا࿚ఴ̤̫ͥͅໝତ͈୭ࠗ་ ତ͉֒ഥঊ͈ழȪࠥȫ͓ͬ̀ນ࡛̯ͦͥȃ̭͈̠͢ͅ ΫΛΠΑΠςϋΈ GA ͉́་ତ͉ၗ८̱͂̀եͩͦͥ ͈́ȄႲ۾ତ͈डഐا࿚ఴ͈ાࣣ͉ഐ୨͉͂࡞̢̞̈́ȃ ̭͈̠̈́͢࿚ఴͅచ̱͉̀Ȅ୭ࠗ་ତ͈Ⴒ̦ږ ༗̯ͦͥତα·ΠσȪ་ତ̦ n ͈ાࣣ͉ n ষࡓ͈α· Πσȫͬஅఘͅဥ̞ͥତ֒ഥഎͺσΌςΒθȪ ତ GA ͂ઠ̳ͥȫ̦մ̯̞ͦ̀ͥȃତ GA ͉́ ̥ͣঊ଼̳ͬͥए༹͈༷͜ΫΛΠΑΠςϋΈ GA Ȫ֒ഥঊႥͬဥ༹̞༷ͥȫ͉͂։̞̈́̽̀ͥȃତ GA͉୭ࠗ་ତͅତͬဥ̞̭ͥ͂́࿒എ۾ତ͈Ⴒ ͬࣉၬ̱̹ౝ̦॑خෝ̜́ͥത̥ͣȄΫΛΠΑΠςϋ Έ GA ͂ڛ̱̀ၻࢡٜ̦̈́ං͈ͣͦͥ͂༭࣬9ȫ̦̜ ̦ͥȄ3 ͈̾ GA ͬڛ̱̹ࡄݪ႕͉ઁ̞̈́ȃུࡄݪ͉́Ȅ ාۼݽႻ၌ף̦डఱ͂̈́ͥݽ߿͈डഐ৽ါ࿒̞̾ͅ ̀ඵ͈̾ GAȄ̳̻̈́ͩΫΛΠΑΠςϋΈ GA ͂ତ GA̞̾̀ͅڛ൦̱̹ȃ ་ତ͂ࡢఘ ΫΛΠΑΠςϋΈ GA ͉́་ତ xi͉Ȫ1,2ȫ͈Ⴅ́ນ ̯ͦȄࡢఘ yjȪj=2~mȫ͉ xiȪi=2~nȫͬ 2 Ⴅ͓̀ͅນ ࡛̯ͦͥȃ̭̭́Ȅn ͉་ତ͈ତ́Ȅm ͉ࡢఘତ̜́ͥȃ ତ GA ͉́Ȅڎࡢఘ yj͉་ତ xi଼͈̳ͬ͂ͥ n ষࡓα·Πσ ́ນ̳ȃࡢఘତ m ͉ഐܽ ̯ͦͥȃ ఼͈ ֒ഥഎͺσΌςΒθ͉͈ૺاͬ࿅༩̱̹डഐا ͺσΌςΒθ̜́ͥȃ͉ତ୷ଲయ̹ͩ̽̀ͅȄਬ౬ ͈ಎ̥۪ͣޏͅഐ̧ࣣ̱̹͈̦͜ॼͤȄഐ̧ࣣ̞́̈́ ͈͉͜ൕఋ̯̞̩ͦ̀7ȫȃ֒ഥഎͺσΌςΒθ͈ίυΓ Αͬ Fig.2 ͅা̳̦Ȅ֒ഥഎͺσΌςΒθ̴͉́͘Ȅഐ ൚̈́ࡢఘତ̥ͣ̈́ͥܢਬ౬ͬैͥȃܢਬ౬͈ڎࡢఘ ͈௺Ȫ་ତ͈ȫ͉၄ତͬঀ̽̀ρϋΘθͅࠨ̯ͦ ͥȃষͅڎࡢఘ͈ഐ؊ഽȪ࿒എ۾ତȫͬບث̱Ȅഐ؊ഽ ̦̞ࣞࡢఘͬষଲయͅঊఃͬॼ఼̳̱̳͂̀ͥȃ̭ ̧͈͂ਬ౬̱͈͂̀ఉအͬږ༗̳̹ͥ͛ͅȄഐ؊ഽ̦ ड̞ࣞ͜ࡢఘ఼̳͈̩ͬͥ́̈́ഐ؊ഽ͈̞ࣞࡢఘ̦ږ ၚഎ͊ͦͥͅخෝ̦̩ࣞ̈́ͥσȜτΛΠσȜσͬဥ ̞̹ȃΫΛΠΑΠςϋΈ GA ͉̱́͂̀ 3 ࡢఘ఼ͬ ̳̦ͥȄତ GA ͉́ষ͓ͥͅౙ༰ୃܰືए ༹:ȫ ͬनဥ̱̞̹̀ͥ͛Ȃ̱͂̀ 4 ࡢఘ఼̳ͬͥȃ
Fig.1 Flow of genetic algorithm
ए༹!
ΫΛΠΑΠςϋΈ GA ͉́֒ഥঊႥ͈ಎ͈एպ౾ͬ ၄ତͤ͢ͅࠨ͛Ȅ3 ̷̸͈͈͈̾ͦͦ֒ഥঊႥ͈ए
3 מὬȆזઐഓȇ֒ഥഎͺσΌςΒθͥ͢ͅݽႻ၌ףडఱݽ͈৽ါ࿒͈൦ պ౾ո͈ࣛ֒ഥঊ̳̭ͬ۟ͥ͂ͤ͢ͅȄ૧̱̞ 3 ͈̾ ࡢఘȪঊȫ̲ͬͥȃएպ౾ͬໝତ༹༷̜̦৾ͥͥ͜Ȅ ུࡄݪ͉́एպ౾͉ 2 ؿਫ਼̱̹͂ȃ ତ GA ͈ए༹༷̱͉̞̩̥͂̀̾մ̯̞ͦ̀ ̦ͥȄུࡄݪ͉́ౙ༰ୃܰືए9ȫ:ȫͬनဥ̱̹ȃ ༹̭͈༷͉̱́͂̀ 4 ࡢఘ఼̱ͬȂ2 ๔࿒͂ 3 ๔࿒ ͈ࡢఘȪၰ͂ࡤ͐ȫ ͬࠫ͐ೄ͈ਔ༏ͅୃܰ ືͅਲ̽̀ˎ͈̾ঊ ͬୃܰ၄ତͤ͢ͅږၚഎͅ ଼̳ͥȃ ȁ ȁȁ Ȫ2ȫ ̭̭́Ȅȁ ȁ ȁȁ Ȫ3ȫ ȁ ȁ ̜́ͥȃ̹͘ȂD ͉ల 4 ͈̥ͣၰͬࠫ͐ೄ͒ئͧ ̱̹ೄݻၗ́Ȅ ͉ၰͬࠫ͐ೄͅೄ̳ͥ໐ߗ ۼ͈ୃܰೄܖೲα·Πσ̜́ͥȃn ͉୭ࠗ་ତ͈ତ́Ȅ ЄȄϽ͉८ ͈ୃܰ၄ତ̯̽̀ͦͥ͢ͅȃ లˏ͈͉Ȅၰͬࠫ͐ೄ͂ೄ̳༷͈ͥ࢜Ȅೄ ̥͈ͣ༊֊ͬࠨ͈͛ͥͅঀ̠ȃ̱̹̦̽̀లˏ͈ ଼͉̯ͦͥঊ̦ၰ͈௺ͬೄ୪എ̧֨ͅࠑ̪͈́ ͉̩̈́Ȅ̞̠̹̈́ͦ͊ͦৗͬ̾ఈ૽͈ࠬͬई̶ ̀Ȅఉအͬ༗̾൱̧̦̜ͥȃˎ͈̾ঊ͉ၰ͈ಎത ͅచ̱̀തచઠͅࠨ̯ͦͥȃ̭̭́ဥ̞ͣͦͥୃܰ ື͉̦ΔύȄດ༊ओ ͉හփͅ୭̯ ͦȄດ༊ओͬఱ̧̈́͂ͥ͂ͅఉအ̦̩̦ࣞ̈́ͥਓ ௵̦ಁ̩̹̈́̽ͤȄං̥̹̳̭̦̜ͣͦ̈́̽ͤͥ͂ͥȃ ȁए͉ m/3 ٝ߫ͤ༐̱̀ m ࡢ͈ঊރ଼̳ͬͥȃഐ ࣣ͈̞ࣞࡢఘ͉ةഽ఼̧̱̯̭̦͂̀ͦͥ͂ܳ͜ ͥȃଲయତ̦ૺ͚͂൳̲ͬ̾ঊ̦௩̢̀ਬ౬̱͂̀ ͜ఉအ̦೩ئ̱̀ȄޫఱȪޫਫ਼ٜȫͅۿ̭̦̜ͥ͂ ͈ͥ́ಕփ̦ຈါ̜́ͥȃ ඏட་։ ΫΛΠΑΠςϋΈ GA ͉́Ȅڎଲయ̤̞̀ͅڎࡢఘ͈ ڎ֒ഥঊ̞֚̾̀ͅအ၄ତͤ͢ͅږၚഎͅඏட་։̥̓ ̠̥ͬࠨ̱Ȅڂ൚̷̧̳͉͈ͥ͂֒ഥঊ͈̦ 1 ͈͂ ̧͉ 2 ͅȄ2 ̧͈͉͂ 1 ͅഢ̵̯ͥȃତ GA ͉́Ȅ ڎଲయ̤̞͈̀̀ͅࡢఘ͈ڎ་ତ̞֚̾̀ͅအ၄ତͅ ͤ͢ږၚഎͅඏட་։̥̠̥̓ͬࠨ̳ͥȃඏட་։ͅ ڂ൚̱̹ࡢఘ͈་ତ͈͉૧̹ͅ၄ତͤ͢ͅρϋΘθͅ ୭̳ͥȃඏட་։̧̦ܳͥږၚ͉ထ͛୭̱̤̩̦̀Ȅ ུࡄݪ͉́ 2/211 ̱̹͂ȃ ଲయయ! ᰴઍߦᱷߔߣߒߡ㧘ⷫߣ↢ᚑߐࠇߚሶ߆ࠄߥ ࠆኅᣖߩਛ߆ࠄᦨ⦟ߩ 3 ࠍㆬ߱ᣇᴺ߿㧘ⷫߣሶߩ㧞 ઍߩో߆ࠄᦨ⦟ߩࠍ㗅ߦㆬ߱ᣇᴺ㧘ⷫ㓸࿅ߩ ㆡᔕᐲߩૐߣሶࠍࠇᦧ߃ࠆᣇᴺߥߤ㧘 ߊߟ߆ߩઍઍࡕ࠺࡞߇ឭ᩺ߐࠇߡࠆ߇21ȫ 㧘ᧄ⎇ ⓥߢߪࡆ࠶࠻ࠬ࠻ࡦࠣ GA ߢ߽ታᢙ୯ GA ߢ߽න⚐ ߦ㧘↢ᚑߐࠇߚሶࠍⷫߦઍࠊߞߡᰴઍߦᱷߔᣇᴺࠍ ណ↪ߒߚޕ डഐȪडఱȫ ထ͛߫ͤ༐̳ଲయତͬ୭̧̱̤̀Ȅ̴͘ڎଲయ͈ಎ ́डഐȪडఱȫ͂̈́ͥଲయडഐȪଲయडఱȫͬ ݥ͛Ȅडࢃ͈̀ͅଲయडഐ͈ಎ̥ͣडਞഎ̈́डഐ Ȫडఱȫ͂̈́ͥࡢఘȪडഐٜȫͬ͐ȃ ႀ֖ٸ་ତ͈ե̞ ΫΛΠΑΠςϋΈ GA ͉́Ȅ͂ͤං̧͓ͥ་ତ͈ํս ͉֒ഥঊႥ͈ΫΛΠତ̽̀͢ͅ୭̯̞̥ͦ̀ͥͣए ً͈́་ତ̦ႀ֖ٸ̭͉̞̦ͥ͂̈́ͅȄତ GA ͉́Ȅထ͛୭̯̹ͦ་ତႀ֖͈ޏٮັ߃ͅഐ؊ഽ͈ άȜ·̦̜ͥાࣣȄଲయତ̦ఉ଼̩̯̹̈́ͥ͂ͦঊ͈ ་ତ͉ႀ֖ٸ̭̦̜ͥ͂ͥͅȃ̭͈̠̈́͢ાࣣ͈৾ͤ ե̞̞̾̀ͅ 3 ༹͈༷̞̾̾̀ͅ൦̱̹ȃ A༹ȇঊࡢఘ͈̜ͥ་ତ ̦୭̯̹ͦ་ତ͈ႀ֖ ಼̢̹ͬͣȄ͉͂۾߸̩̈́٨֚͛̀အ၄ତͤ͢ͅȄ୭ ̯̹ͦ་ତ͈ႀ֖ඤ̠̈́ͥ͢ͅͅ ͈ͬࠨ͛ͥȃ B༹ȇঊࡢఘ͈̜ͥ་ତ ̦୭̯̹ͦ་ତ͈ႀ ಼̢֖̹ͬͣȄ಼̢̹ݻၗ͂൳̲ၾ̺̫Ȅႀ֖͈ޏٮ ̥ͣႀ֖ඤ͒୬ͤ༐̳ȃ̳̻̈́ͩȄ་ତ ͈ํս̦ ̧̜́ͥ͂Ȅ଼̯̹ͦঊ͈ ̦႕̢͊ ͤ͢͜ఱ̧̞̺̹̳̽͂ͥ͂Ȅ૧̱̞ ͉ষ͈̠͢ ͅ٨͛ͥȃ ȁ Ȫ4ȫ ତࡑ ତ GA ̞̾̀ͅȄA ༹ȄB ༹͈Ⴆͬऔ̳̹ͥ ͈֚͛̾ͅαϋΙζȜ·࿚ఴ̞̾̀ͅڛࡑ࣐ͬ̽ ̹ȃαϋΙζȜ·࿚ఴ̱͉͂̀Ȅఉအͬບث̳̹ͥ͛ ͅఉ༰۾ତ̜́ͥ Rastrigin ۾ତͬဥ̞̹22ȫ ȃ ȁȁȁȁȁȁ ȁȁȁ Ȫ5ȫ Rastrigin۾ତ͈ఘഎ̈́အঊ̹ͬͥ͛ͅȄ୭ࠗ་ତ ̦ˎࡢȪn=3ȫ͈ાࣣ̞̾̀ͅȄ་ତ x2Ȅx3ͅచ̳ͥ۾ ତ͈་൲ͬ Fig.3 ͅা̳ȃႀ֖ඤͅఉତ͈άȜ·̦ం ह̱̞̦̀ͥȄ̞͈ࣞάȜ·͉ႀ֖͈ਔ༏ັ߃̜ͥͅ ̭̦̥͂ͥȃ̭͈̠͢ͅఉତ͈άȜ·̦̜ͥ۾ତ͈ા ࣣ͉Ȅडഐٜ̱͂̀ౝऔ̯̹͉ͦޫਫ਼ٜȪޭఱȫͅ
ۿ̳̞͈ͤ́͞Ȅ૯͈डഐٜȪडఱȫͬං̹͉ͥ͛ͅ ਬ౬̱͈͂̀ఉအ̦ਹါ̈́ͥͅȃA ༹͂ B ༹͈ఉအ ͓̹ͬͥ͛ͅତࡑͬঔ̱̹ȃུࡑ͉́Ȅౙ ༰ୃܰືए͈ດ༊ओͬ 1.56Ȅࡢఘତ 41Ȅ་ତ 3Ȅ ଲయତ 41Ȅඏட་։ၚ 2/2111 ͂୭̱Ȅ̷̸ͦͦ 4 ٝ ͈দ࣐ࠗॳ࣐̹ͬ̽ȃ
Fig.2 Rastringin function of x1 and x2
Fig.4 ͂ Fig.5 ̷̸͉ͦͦ A ༹͂ B ༹ͥࠗ͢ͅॳࠫض́Ȅ ڎଲయ͈ଲయडఱͬা̱̹͈̜́ͥ͜ȃडਞഎ̈́ड ఱȪडഐٜȫͅൢో̳ͥଲయ͉ڎদ࣐ࠗॳ́։̦̈́ͥȄ ̴̞͈ͦদ࣐́͜डਞഎ̈́डఱ̱͉͂̀ 91 ͅ߃̞ ̦ං̤ͣͦ̀ͤȄA ༹͂ B ༹ͅႦ͉̞̈́ȃ̭͈࿚ఴ͈ ાࣣ͉ 41 ଲయ̩̞͈ͣ́ࠗ͘ॳ̳ͬͦ͊ȄडఱȪड ഐٜȫͬං̧̭̦ͥ͂́ͥȃ̱̥̱Ȅ་ତ̦௩̢ͥ͂ଲ యତͬఱ̧̩୭̱̞̈́͂डఱȪडഐٜȫ͉ංͣͦ̈́ ̩̈́ͥȃ
Fig.3 History of maximum value for each generation by A method
Fig.4 History of maximum value for each generation by B method
Fig.6 ͂ Fig.7 ͉ȄA ༹ ͂ B ༹ ͅ ̤ ̞ ̀Ȅ21Ȅ31Ȅ41 ଲయ࿒̤̫ͥͅ 41 ࡢఘ͈ືͬ x2Ȅx3͈जດͅা̱̀ ̞ͥȃA ༹͉́ଲయ̴̥̥ͩͣͅႀ֖ඤͅࢩ̩ື̱ ̞͈̀ͥͅచ̱ȄB ༹͉́ଲయ̦̩̜̯ࣞ̈́ͥ͂ͥ̈́ ႀ֖ͅਬಎ̱̞̭̦̥̀ͥ͂ͥȃ̳̻̈́ͩ B ༹͉́Ȅ ࡢఘ̦ޫਫ਼എ̈́άȜ·ਔ༏ͅਬ̧̞͈̽̀̀ͥ́͘Ȅఉ အ͈۷ത̥͉ͣ A ̢༹̦̞̞ͦ̀ͥ͂ͥȃఉအ ͬা̳ঐດ̱͂̀་ତ͈८ͬڛ̳ͥ͂ Table 2 ͈͢ ̠̈́ͤͅȄA ༹͉ଲయۼ͈ओ̦ઁ̞͈̈́ͅచ̱̀ȄB ༹ ͉ 21 ଲయ࿒͓̀ͅ 31Ȅ41 ଲయ࿒͉́ 2 ̯̩ࠥ̈́̽ ̀Ȅ་ତ͈८̦̯̩ͣ͊ͤȄఉအ̦అ̞̈́ͩͦ̀ͥ ̭͂ͬা̱̞̀ͥȃ
Fig.5 Distribuion of variables x1 and x2 at three generations by A method
Fig.6 Distribuion of variables x1 and x2 at three generations by B method ତ GA ུ̞࣐̹̾̀̽ͅତࡑ͈ࠫض͉ A ༹ ͂ B ༹͕͖́൳̲̠̈́͢डఱȪडഐٜȫͬං̞̦̀ͥȄ ఉအ̞̠͂۷ത̥͉ͣ A ༹̦̞ͦ̀ͥ౯̯ͦͥ ͈́Ȅոئ͉́ A ༹ͬनဥ̱̹ȃ ̧͘࿌ݽ͈डഐ߿ ȪԅȫȁତGA ͂ΫΛΠΑΠςϋΈ GA ͈ڛ ୭ࠗ་ତ Πϋତ 291 Πϋ̧͈͘࿌ݽͬచયͅාۼ၌ףڣ̦ डఱ͂̈́ͥ߿͈৽ါ࿒ͬȄGA ͬဥ̞̀ౝऔ̳̭ͥ͂ ͬদ͙̹ȃ୭ࠗ་ତ͉ಿ LȄ໙ BȄْࠗݎକ dȄ ఘಎ؇౯࿂߸ତ CmȄպ౾ lcbȄْ̤͍ࠗ͢ેఠ̤ͅ ̫ͥਉକ͈වৣڙഽ iE ̜́ͥȃ̭͈ͦͣ୭ࠗ་ତ͉ Oortmerssen23ȫͥ͢ͅࢯଔͅဥ̞ͣͦͥ་ତ̜́ͥȃ
5 מὬȆזઐഓȇ֒ഥഎͺσΌςΒθͥ͢ͅݽႻ၌ףडఱݽ͈৽ါ࿒͈൦ ࿒എ۾ତ ୭ࠗ་ତͬဥ̞̀ GA ͈࿒എ۾ତ̜́ͥාۼ၌ףڣ ͬଔ̳ͥͬষ͈̠͢ͅ൵̞̹ȃ ȁාۼ၌ףڣ ȁȁȁɁාۼਓවڣȽාۼڣȁȁȁ Ȫ6ȫ ȁාۼਓවڣ ȁȁȁɁݿثȿාۼݽڕၾ ȁȁȁ Ȫ7ȫ ݿث͉ݿਅ͞ܬ୯Ȅ̷͈ఈ͈২ٛૂସ̽̀͢་൲̳ͥ ̦Ȅݽݽުͥ͢ͅාۼਓවڣ͈ॳ͉ͅݿثͬ ୭̱̀Ḙ̏ͦͅݽڕၾ̲ͬ̀ݥ̭̱̹͛ͥ͂ͅȃ ාۼݽڕၾ͉࣎ˍٝ൚̹͈ͤݽڕၾͅාۼ࣎ٝ ତ̲̹͈̱̹ͬ͂͜ȃ ȁාۼݽڕၾ ȁȁɁˍ࣎٬൚̹͈ͤݽڕၾȿාۼ࣎ٝତ ȁȁ Ȫ8ȫ ݿث̧͘͞࿌ݽު͈ාۼ࣎ٝତȄݽા؉໘শۼ൝͉ 3112 ාഽ͈ݽުฒ23ȫ ൝ͬ४ࣉ̱̀ͅȄոئ͈̠͢ͅ ୭̱̹ȃ ݿث̞͉̾̀ͅȨ̏͘࿌ݽު́ఉ̩ݽڕ̯ͦͥϋζȄ ͺΐͬݽڕచયݿ̱͂̀Ȅݿثͬ 81Ȫ /kgȫ͂୭ ̱̹ȃාۼ࣎ٝତ͉Ȅݽުͬ߃٬̥Ȅ̹͉͘ဢ́ ࣐̠̥ͤ͢ͅఱ̧̩։̦̈́ͥȄུࡄݪ͉́ဢݽު́ා ۼ࣎ٝତͬ 7 ̱̹ٝ͂ȃˍ࣎٬൚̹͈ͤݽڕၾ͉Ȅݿ ாယୟ͈ 86% ͈ݽڕၾͬเͥ͢ͅ 2 ͈ٝၾ͂ ̱Ȅٝତͬ 8 ̱ٝ͂̀Ȫ9ȫ́ݥ͛ͥȃ ȁˍ࣎٬൚̹͈ͤݽڕၾ [t] ȁȁɁ 1.86[ ] ȿ [ ݿாယୟ ]ȶ ȷȿ˓ ȁ Ȫ9ȫ ݿாယୟ͉߿৽ါ࿒൝̥ͣȪ:ȫ́߃য̳ͥȃ ȁݿாယୟ ȁȁɁ 1.3 ȿ Cbȿ L ȿ B ȿ D Ƚ 31ȶ ȷ ȁȁȁȁ Ȫ:ȫ ̭̭́ȄD ͉૬̯ȄCb͉༷ࠁ߸ତ̜́ͥȃCb͉ෳକ ၾ Ϛ ͤࠗ͢ॳ̱̹ȃD ̤͍͢Ϛ ͉ݽ͈ΟȜΗͤ͢Ȅ Fig.8 ̤͍͢ Fig.9 ͅা̳̠͢ͅȄݎକ d ̤͍͢Πϋ ତ T ͈۾ତ̱͂̀ٝܦȪ21ȫȄ Ȫ22ȫ ͤ͢ͅଔ̳ͥȃ ȁȁ ȁȁ Ȫ21ȫ ȁ Ȫ22ȫ
Fig.7 Relationship between depth and draft
Fig.8 Relationship between displacement and gross tonnage
ষͅȄාۼڣ͉ႻೈȄීၳȄࡘثરݕȄݽ Ⴛאު̷̤͍͈͢ఈȪ̢̯యȄݽߓȆݽ͈༞ਘȄ ༗ࡏ̈́̓ȫࣣ̱̹ͬࠗ߄ڣ̱͂̀ȄȪ23ȫͤ͢ݥ͛ͥȃ ȁ[ ාۼڣ ] ȁȁɁ [ ݽႻคષࡔث ] ȼ [ ݽႻאު ] Ȫ23ȫ ȁ[ ݽႻคષࡔث ] ȁȁɁ [ Ⴛೈ ] ȼ [ ࡘثੲݕ ] ȼ [ ීၳ ] ȁȁȁȁȼ [ ̷͈ఈ͈ࠐ ] Ȫ24ȫ ུࡄݪ͉́Πϋତ T ͬ 291 Πῧ̱̞̹ܰ̀ͥ ͛Ȅݽ͈৽ါ࿒̦་ا̱̀͜ழ֥ତ͉͕͖֚͂ࣉ ̢̀ȄႻೈ͉ාۼਓව͈ 51ɓ͂୭̱̹ȃ̹͘Ȅݽ Ⴛאު͜ාۼਓව͈ 21% ͂୭̱̹ȃ̷͈ఈ͈ࠐ ͉ݽႻคષࡔث͈ 41% ̱̹͂ȃ ȁ[ Ⴛೈ ] Ɂ [ ාۼਓව ] ȿ 1.5ȁ Ȫ25ȫ ȁ[ ݽႻאު ] Ɂ [ ාۼਓව ] ȿ 1.2 Ȫ26ȫ ȁ[ ̷͈ఈ͈ࠐ ] Ɂ [ ݽႻคષࡔث ] ȿ 1.4ȁȁ Ȫ27ȫ ȁ ࡘثੲݕ͉ثͬੲݕාତ́ڬ̹͈̽́͜Ȅུࡄ ݪ͉ੲݕාତͬ 26 ා͂ب̱̹ȃث͈ଔ͉ͅ Ȫ29ȫ ͬဥ̞̹ȃK2ȡ K5͉ثͬ߃য̳̠ͥ͢ͅା̱ ̹߸ତ̜́ͥȃ ȁ[ ࡘثੲݕ ] Ɂ [ ث ]/[ ੲݕාତ ] Ȫ28ȫ Table 1 Comparison of variance between A method and B method
variables x1 x2
method A method B method A method B method 10th generation 5.34 4.58 5.93 4.47
20th generation 8.02 0.14 5.66 0.14
ȁ Ȫ29ȫ Ȫ29ȫֲ͈༏ల 2 ͉ࣜڔࢥैͅ႕̳ͥࢥ́Ȅ ٸโ࿂ୟ͂ࢥତ̦̥̥ͥਉ͈ಿ͛ͥͅڬࣣͬা ̳ B/L ͬঐດ̱̞͂̀ͥȃडࢃ͈͉ࣜඤࢥম͞୭ ̈́̓Ȅఘ͈ఱ̧̯ͅ۾߸̳ͥࠐȪࢥȄ୭̈́ ̓ȫͬນ̱̞̀ͥȃ৽ܥثڒ͉ႁ͕͖ͅ႕̳ͥ͂߃ য̱̹ȃ༞ܥ̈́̓͜৽ܥ͈ఱ̧̯ͅ႕̳̱ͥ͂̀Ȅ߸ ତ K3́ା̱̹ȃ BHP͉࣎٬௸ႁ V ͈শ͈ఘࢯ Rt͂ଔૺ࢘ၚϽ͢ ͤȄȪ2:ȫͤ͢ݥ͛ͣͦͥȃ ȁ Ȫ2:ȫ ݽ߿ͬచય̱̀ͅȄ۰ౙ́ڛഎഐဥ̦̞ࣞ ࢯଔ༹̱͂̀Ȅvan G.Oortmerssen23ȫ ͈ଔͬဥ̞̹ȃ ̹̺̱Ȅ̞̩̥͈̾ݽ߿̞͈̾̀ͅକࡑࠫض͂ ڛ̱̀Ȅࡔა̜ͥͅ߸ତ c4͈ࣜͬજ̞̹Ȫ31ȫ ͬनဥ̱̹ȃ ȁ ȁȁȪ31ȫ ̭̭́ȄϚ͉ෳକၾȄS ͉૫କ࿂ୟȄЇ͉ၠఘྟഽȄ Fn͉έσȜΡତȄRn ͉τͼΦσΒତ̜́ͥȃm,c2,c3,c5 ͉ L/BȄCp ൝͈߿ΩριȜΗ͈۾ତ̱͂̀ນ̯ͦͥ 23ȫ ȃ૫କ࿂ୟ S ͉Ȫ32ȫͤ͢ͅ߃য̱̹ȃ ȁ ȁȁȁȁȁȁȁȁȁȁȁȁ ȁȁȪ32ȫ ̧͘࿌ݽ̞̾̀ͅȄȪ31ȫ͈ଔ͂ٝၠକ ࡑࠫض͈ڛͬ Fig.:ȪaȫͅȄݽުႯਠ̞͈̾̀ͅ ڛͬ Fig.:Ȫbȫͅা̳̦Ȅଔ͉ࡑ̩֚͂͢౿̱ ̞̀ͥȃఈ͈ତୗ͈ݽ߿̞͈̾̀ͅڛ̥ͣȄଔ Ȫ31ȫུ͉ࡄݪͅঀဥ̧́ͥ͂౯̱̹ȃ̤̈́Ȅଔૺ ࢘ၚ ͉ 1.7 ͂ب̱̹ȃ
Fig.9(a) Comparison of EHP between estimate and experiment for a fi shing vessel
Fig.9(b) Comparison of EHP between estimate and experiment for a training ship ষͅීၳ̞̾̀ͅࣉ̢ͥȃීၳ͉ීၳકၾͅී ၳثڒ̲̹͈ͬ́͜Ȫ33ȫͤ͢ͅݥ͛ͣͦͥȃ ȁ[ ීၳ ] Ɂ [ ීၳકၾȪͰȫ] ȁȁȿ [A ਹثڒȪ / Ͱȫ] [ ] Ȫ33ȫ ̭̭͉́ȄA ਹثڒ͈֚ͬ 41 / Ͱ̱̹͂ȃීၳ કၾ͉ීၳકၚȄA ਹ͈ྟഽ Ȅ৽ܥ෯ႁ BHP ̤͍࣐͢শۼͤ͢Ȫ34ȫ́ݥ͛ͣͦͥȃ [ීၳકၾ ] Ɂ [ ීၳકၚ ]/ ȿ BHPȪkwȫȿ [ ාۼ࣐শۼȪhȫ] [ Ͱ ] Ȫ34ȫ ීၳકၚ͉ 2111 ȡ 3111kw ·ρΑ͈ΟͻȜΔσ ϋΐϋ͈ୡ̥ͣ 1.3[kg/ȪkwȆhȫ] ̱̹͂ȃ̹͘ȄA ਹ͈ྟഽ͉ Ɂ 1.96[kg/ Ͱ ] ̱̹͂ȃȁ 2 ͈ٝ࣎٬͉́Ȅݽાْ́ͬࠗ͘௸ႁ V ́Ȅ˒ ۼ́؉໘࣐̱࣎Ȅௌު͉ːˌۼ̱͂̀Ȅௌުಎ͈ීၳ ક͉ڒႁ͈ 76ɓ͂ب̱̀Ȅْࠗ௸ႁ̱́࣎ ̹শۼ۟ͅॳ̳ͥȃ֚ාͅ˒̳̱ٝ࣎ͥ͂̀Ȅාۼ ࣐শۼͬ 5711 শۼ̱̹͂ȃ ଷૄ ࠗॳࠫض̦षഎ̠̈́͂̈́ͥ͢ͅȄ୭ࠗ་ତ͈ํս ͬئܱ͈̠͢ͅ୭̱̹ȃ L=39~54 [m] B=7~8.6 [m] d=3~4.6 [m] Cm=1.97~1.:76 lcb=Ƚ 4.3~2.1 [%] iE=29~39.6 [deg.] ̹͘Ȅ৽ါ࿒̥ͣ GM ͬଔ̱Ȅ໘ࡔͬࣉၪ̱̀ GM͈नͤංͥํսͬ୭̱̹ȃਹ̯ࣞͬ KGȄ ̯ࣞͬ KBȄιΗΓϋΗȜࠂͬ BM ̳͂ͥ͂Ȅ ȁ ȁȁȁȁȁȁȁȁȁȁȁȁȪ35ȫ ̜́ͥȃKGȄKBȄBM ͉Ȫ36ȫ́߃য̱̹ȃ Ȫ36ȫ ȁ ྖशેఠ͈ GM ͈ئࡠ͉Ȅఱߜ25ȫ ͈ޗشܱͅश̯ ̞ͦ̀ͥݽ͈ GM ͈ํսͬ४ࣉ̱̹ͅȃષࡠ͉ͤ ౷͈۷ത̥ͣȄ͈؍ဝͦਔܢ̦ 5 ຟոષ̱͂̀Ȅ GM͈ํսͬȪ37ȫ͈̠͢ͅ୭̱̹ȃ ȁ ȁȁȁȁȁȁȁȁȁȁȁȁȁȪ37ȫ
7 מὬȆזઐഓȇ֒ഥഎͺσΌςΒθͥ͢ͅݽႻ၌ףडఱݽ͈৽ါ࿒͈൦ ࠗॳࠫض ْࠗȪ࣎٬ȫ௸ႁ̦ 9 ΦΛΠ͂ 21 ΦΛΠȄ23 ΦΛΠ ͈ 4 ΉȜᾼ̞̾̀Ȅࡢఘତͬ 61Ȅଲయତͬ 2111Ȅඏ ட་։ၚˍ/211 ̱͂Ȅତ GA ͉́ດ༊ओ Ȅ ̴͉̞ͦ͜ 1.4 ̱͂̀ࠗॳ̱̹ȃତ GA ͂ΫΛΠΑ ΠςϋΈ GAȄ̷̸ͦͦ 3 ̴ٝ̾ࠗॳ̱̀Ȅාۼ၌ף ڣ͈डఱ͓̹ͬȃࠗॳࠫضͬ Table 3 ͅা̳ȃ၌ף डఱ̷̸͈͈ͦͦ 3 ͈ٝদ࣐͈ओ͉ȄΫΛΠΑΠςϋ Έ GA ͈ 23 ΦΛΠ͈ાࣣ͉ 21ɓ͂ఱ̧̩Ȅ̷ͦոٸ ͉ 2~6% ոඤ̜́ͥȃ̹͘ȄΫΛΠΑΠςϋΈ GA ͢ͅ ͥ၌ףडఱ͉͈̀௸̤̞̀ͅତ GA ͈ࠫض͢ ͤ͜৹ۙఱ̧̞ȃ௸̦̩͕ࣞ̈́ͥ̓၌ף͉ઁ̩̈́̈́ͥ ̴̭͉̞͈͂ͦ GA ́͜൳̲̜̹́̽ȃ ତ GA ͂ਲြ͈ΫΛΠΑΠςϋΈ GA ́Ȅ̷̸ͦ ͈ͦාۼ၌ף̦डఱ͂̈́ͥݽ߿͈৽ါ࿒ͬ Table 4 ͅা̳ȃུࠗॳ͉ȄΠϋତ̦̞̠֚͂ૄ࣐́̽ ̞̀ͥȃ̭͉ͦȪ22ȫͤ͢Ȅෳକၾ̞̠֚͂ૄ͈ ئ́ාۼ၌ף̦डఱ͂̈́ͥಿ͞໙Ȅݎକͬݥ̭͛ͥ ͂̈́ͥͅȃତ GA ͂ΫΛΠΑΠςϋΈ GA ͈ࠫضͬ ڛ̳ͥ͂Ȅාۼ၌ף͉ Fig.21 ͅা̳̠͢ͅၰ৪͈ ओ͉̯̞ȃ߿́ڛ̳ͥ͂Ȅಿ L ͉ၰ৪ͅఱ̧̈́ ओ͉ࡉ̴ͣͦȄ୭̱̹ํս͈ષࡠͅ߃̞̦Ȅ໙ B ͉ΫΛΠΑΠςϋΈ GA ͈༷̦ఱ̧̩Ȅݎକ d ͉ݙͅ ତ GA ͈༷̦ఱ̧̞ȃ̳̻̈́ͩȄତ GA ͈ࠫض͉ ΫΛΠΑΠςϋΈ GA ͈ࠫضͤ͢͜ L/B ̦ఱ̧̩ȄB/d ̦̯̞߿̞̈́̽̀ͥͅȃ̭͈̠͢ͅȄ3 ͈̾ GA ́ ං̹ͣͦ໙ B ͂ݎକ d ͉̥̈́ͤ։̦̈́ͥȄාۼ၌ף ڣ͈ओ͉̯̥̹̽ȃ̭͉ͦ࿒എ۾ତȪාۼ၌ףȫ̦Ȅ ޭఱ̦ໝତంह̳ͥఉ༰۾ତ̜̹́ͥ͛ͅȄ3 ͈̾ GÁ་ତȪ߿৽ါ࿒ȫ͉ͅओ։̦̲̹̦Ȅංͣ ̹ͦޭఱȪडఱ၌ףڣȫ͈ओ̦̯̞̹̜͛́ͥȃ̳ ̻̈́ͩȄޭఱٜ̦ͬ͂ͥఉତంह̳͈ͥ́Ȅਓ௵ٜȪड ఱȫͬං͈̦ͥඳ̱̞̦Ȅͤ͢ၻ̞ٜͬං̹͈ͥ͛ΜȜ σ̱͂̀ GA ͉ခဥ̢̜̞́ͥ͂ͥȃ
Fig.10 Comparison of fi shery profi ts of the optimum ships obtained by two GA methods
Table 2 The maximum profi t obtained by real coded and bit-string GA methods unit: thousand yen
Ship speed [kt] 8 10 12
Trial No. 1st 2nd 1st 2nd 1st 2nd Real coded GA 29007 27471 21100 20838 8222 8164
Bit-string GA 29121 28917 22687 22302 10153 9140
Table 3 Principal particulars of the optimum ships and their profi ts
Ship speed [kts] 8 10 12
Method of GA Real coded Bit-string Real coded Bit-string Real coded Bit-string L [m] 42.00 43.00 42.50 43.00 42.80 43.00 B [m] 6.05 7.20 6.55 7.20 7.25 7.20 d [m] 2.72 2.20 2.71 2.40 2.70 2.50 Cm 0.945 0.965 0.955 0.965 0.960 0.965 lcb [%] 0.21 -1.00 -0.74 -1.10 -1.02 -1.50 iE [deg.] 28.3 28.5 22.1 28.5 26.4 18.0 Cp 0.729 0.725 0.661 0.664 0.592 0.638 Rf [N] 6895 6539 10482 9804 14755 13737 Rw [N] 500 4614 7213 6374 17599 19602 Rt [N] 7395 11153 17696 16178 32354 33338 BHP [kW] 51 77 151 139 333 343
Total Catch [ton] 2723 2906 2726 2827 2729 2792 Income [106 yen] 190.6 203.4 190.8 197.8 191.0 195.4
Fuel cost [106 yen] 1.6 2.5 4.9 4.5 10.8 11.1
௸͈֑̞ͥ͢ͅ߿৽ါ࿒͈་اͬࡉͥ͂Ȅ௸̦ ௸̩̈́ͥ͂ಿ L ͞໙ B ͉ະ་̥Ȅ̴̥̯̩ͩ̈́ͤͅȄ ݎକ d ͉ఱ̧̩̞̈́̽̀ͥȃFig.22 ͅ Cp͈ڛͬা̱̀ ̞̦ͥȄ̴̞͈ͦ GA ́͜௸̦௸̩̈́ͥ͂ Cp͉̯ ̩̈́̽̀Ȅ௮෨ࢯ Rw ̯̩̱̞ͬ̀ͥȃ༷֚Ȅݿᦺ ̦৹̯̩̹ۙ̈́ͥ͛ݽڕၾ͉ࡘͤȄਓව̦ࡘઁ̳ͥȃུ ࠗॳ͉́Ȅݽڕၾ͉ݿᦺ͈͙ͅऒֲ̯ͦ̀Ȅ௸͈גޣͬ ࣉၪ̱̞̞̀̈́ȃ̳̻̈́ͩȄ௸̦௸̞͂ݽા்̩̞ͅ ̀ݽުশۼ̦௩̢̀ݽڕၾ̦௩̢ͥȄ்̜̞͉̩ͥܦࢽ̱ ̞̀ࣞݿث́คͦͥȄ͈̈́̓၌തͬࣉၪ̱̞̞̹̀̈́͛Ȅ ௸͈גޣ̦ഐ୨ͅບث̯̞̞͈ͦ̀̈́́Ḙ̏ͦͣͬࣉၪ ̳ͥ͂ͤ͢ఏ൚̈́ࠫض̦ංͣͦͥ͂এͩͦͥȃ
Fig.11 Comparison of Cp of the optimum ships obtained by two GA methods ̤̈́Ȅུࠗॳ႕͉́Ȅତ GA ͂ΫΛΠΑΠςϋΈ GÁ͉ྶږ̈́Ⴆ͉̥̹͛ͣͦ̈́̽ȃ ȪԆȫȁීၳثڒ͈גޣ ࠗॳૄ̈́̓ ߃ ාȄ ࡔ ث ڒ ͈ ࣞ ൯ ͅ ͢ ͤȄ ݽ ͅ ঀ ဥ ̯ ͦ ͥ ਹثڒ͜ݢ൯̱̀ݽުࠐאͅ૬࣫̈́גޣͬݞ͖̱̀ ̞ ͥȃ ୶ ͈ ൦Ȫ ԅ ȫ ́ ͉ ਹ ث ڒ ͬ 41 / Ͱ ͂ ̱ ̹̦Ȅ211 / Ͱ ̱̹͂ાࣣͅාۼ၌ף̦डఱ͂̈́ͥ ߿̞̾̀ͅତ GA ́൦̱̹ȃ࿒എ۾ତ͈ࠗॳ ͉ਹثڒͬ 211 / Ͱ ͂་ࢵ̱̹ոٸ͉Ȫԅȫ͈ ા ࣣ ͂ ൳ ̲ ͂ ̱ ̹ȃ ୭ ࠗ ૄ ͂ ̱ ̀ȄȪaȫȁ Π ϋ ତ T ͬ ֚ ͈ 291 GT ͂ ̱ ̹ ા ࣣȄȪbȫȁ Π ϋ ତ ͅ ଷ ࡠ ͬ ح ̢ ̈́ ̞ ા ࣣȄ ͈ 3 Ή Ȝ Α ͂ ̱ ̹ȃ ུ ࡄ ݪ ́ ͉Ȅ Π ϋ ତ ͂ ෳ କ ၾ ͉Ȫ21ȫ ͅ ͢ ͤ 2 փ എ ͅ ۾ ߸ັ̫̞͈ͣͦ̀ͥ́ȄΠϋତ̞̠̭͉֚͂͂ෳ କၾ֚Ȅ̞̠̭̜͂͂́ͥȃ୭ࠗ་ତ͉Ȫaȫ͈ા ࣣ ͉ ୶ ͈ ൦Ȫ ԅ ȫ ͂ ൳ ̲ ́ ̜ ͥ ̦ȄȪbȫ ́ ͉ ༷ ࠁ ߸ତ Cbͬ་ତ̱͂̀ح̱̹ȃCb͈৾ͤංͥํս͉ ȁȁȁ1.6< Cb <1.86ȁ ȁȁȁȁȁȁȁȁȁȁȁȁȁȪ38ȫ ̱̹͂ȃ ࠗॳࠫض ௸̦ 9Ȅ21Ȅ23 ΦΛΠ̞̾̀ͅȄ̷̸ͦͦ 4 ͈ٝ ࠗॳͬঔ̱̀Ȅ̷͈ಎ͈डఱͬनဥ̱̹ȃࠫضͬ Table 5 ͅা̳ȃාۼ၌ף̞̾̀ͅਹثڒ̦ 41 / Ͱ͈ ࠫض͂ڛ̳ͥ͂ȄFig.23 ͅা̳̠͢ͅȄΠϋତ֚ ͈ૄ͉́ਹثڒ̦̩ࣞ̈́ͥ͂၌ף̦ઁ̩̈́̈́̽̀Ȅ 23 ΦΛΠ͉́ζͼΑ͂̈́ͥȃΠϋତ͈ଷࡠ̴͉ͬ ̳͂Ȅ9 ΦΛΠ͈ાࣣ͉ਹثڒ͈ࣞ൯ͬΨȜ̧́ͥ ̦Ȅ21Ȅ23 ΦΛΠ͉́Πϋତ͈֚ાࣣ͂ओ͉̞̈́ȃ ߿͈ڛ̳ͬͥ͂ȄL/B ͉ Fig.24 ͅা̳̠͢ͅȄ ௸̽̀͢ͅ։̈́ͥ་ا̱̞ͬ̀ͥȃΠϋତ֚́ਹ ثڒ̦ 41 / Ͱ͈ાࣣ͉௸̦௸̩̱̹̦̈́ͥ̽̀ͅ L/B͉̯̩̞͈̈́̽̀ͥͅచ̱̀ȄΠϋତ֚́ ਹثڒ̦ 211 / Ͱ͈ાࣣ͉௸ͥ͢ͅ་ا͉ઁ̩̈́Ȅ Πϋତܰଷྫ̱́ਹثڒ̦ 211 / Ͱ͈ાࣣ͉௸ ̦௸̩̱̹̦̈́ͥ̽̀ͅఱ̧̩̞̈́̽̀ͥȃ༷ࠁ߸ତ Cb͈ڛͬ Fig.25 ͅা̳ȃ௸̦௸̩̈́ͥ͂Ȅ̴̞ͦ ͈ાࣣ͜ Cb͉̯̩̈́̽̀ࢯ೩ࡘͬͥ߿͂̈́̽ ̞̀ͥȃਹثڒ̦ 211 / Ͱ̧͈͂ȄΠϋତܰଷྫ ̱͈ાࣣ͉Πϋତ͈֚ાࣣͤ͢͜ Cb͉ఱ̧̩̈́̽ ̤̀ͤȄݽڕၾ͈௩ح̞ͬ̽̀ͥȃ
Fig.12 Comparison of fi shery profi ts of the optimum ships under various conditions
Fig.13 Comparison of L/B of the optimum ships under various conditions
9
מὬȆזઐഓȇ֒ഥഎͺσΌςΒθͥ͢ͅݽႻ၌ףडఱݽ͈৽ါ࿒͈൦
Fig.14 Comparison of Cb of the optimum ships under various conditions Πϋତ͈ܰଷ̴͉̳ͬ͂Πϋତ͈֚ાࣣ͓ͅ ̀ 9Ȅ21 ΦΛΠ͉́ෳକၾ͉ఱ̧̩̈́ͤȄ23 ΦΛΠ́ ͉̯̩̞̈́̽̀ͥȃ9Ȅ21 ΦΛΠ͉́ࢯ͈௩ح͢ͅ ͥීၳ͈௩ఱͤ͢͜Ȅͬఱ̧̩̱̀ݽڕၾͬ௩̳͞ ༷̦၌ף̦ࡉࣺ̭͛ͥ͂ͬাऐ̱̞̀ͥȃ23 ΦΛΠͅ ̈́ͥ͂ීၳ̦ఱ̧̨̩̳̈́ͤ̀Ȅ̵̯̩̰ͬͥͬ ං̩̞̈́̈́̽̀ͥȃ ȁ ͂͛͘ ུࡄݪ͉́ତ GA ͂ΫΛΠΑΠςϋΈ GA ͈ 3 ̾ ͈ GA ͬဥ̞̀Ȩ̏͘࿌ݽ͈ාۼ၌ף̦डఱ͂̈́ͥ ߿͈৽ါ࿒ͬౝ̱̹॑ȃ̷͈ࠫضȄˎ͈̾ GA ̥ͣݥ͛ ̹ͣͦ߿৽ါ࿒͉৹ۙ։̞̹̦̈́̽̀Ȅාۼ၌ף͉͕ ͖൳ഽ̜̹́̽ȃ̭͉ͦޭఱ̦ໝତంह̳ͥఉ༰ ͈࿒എ۾ତͅచུ̱̀ࡄݪ́नဥ̱̹ GA ̦ခ̜࢘́ͥ ̭͂ͬা̱̞̀ͥȃུࡄݪ͈৽࿒എ̜́ͥडഐ߿ౝݥ ͈͒ GA ͈ഐဥ̞͉̾̀ͅȄͤ͢ၻ̞߿ْ̳ͬࠗͥ ̹͈͛ခף̈́ͥࡉ̦ං̭̦ͣͦͥ͂ږ̧́Ȅඅͅ ତ GA ͉ίυΈρηϋΈ͜ယօ̜̹́ͥ͛Ȅ߿ْࠗ ͈ܢ൦͉ͅခဥ̈́ΜȜσ̈́ͤͅං͈ͥ͂͜এͩͦͥȃ ȁུࡄݪ́࿒എ۾ତ̱͂̀৾ͤષ̬̹ාۼ၌ף͈ࠗॳ༹ ̞͉̾̀ͅڎࣜ࿒͈ଔୈഽ͈࢜ષ͞ࡉೄ̱̳ͬͥຈါ ̦̜ͥȃ႕̢͊Ȅݽڕၾ͞ݿثȄࡔثڒȄႻೈ͈̈́̓ ࠗॳ͉ 3112 ා͈ࡣ̞ၳͅܖ̞̞͈̿̀ͥ́Ȅ࡛ે͂ ͉̥̈́ͤેޙ̦։̞̈́̽̀ͥȃड߃͈ීၳ͈ࣞ൯ͅచ ੜ̳̹͉ͥ͛ͅȄ௸ͬಁ̩̳̭̦ͥ͂ड͜ခ༹༷࢘̈́ ͈̠̜́ͥ͢ȃ̱̥̱Ȅུࡄݪ͉́௸͈ݿث͞ݽڕၾ ͅݞ͖̳גޣ̈́̓ͬࣉၪ̱̞̞͈̀̈́́Ḙ͈̏ത̞̾ͅ ͉̀ࢵͅ൦̦ຈါ̜́ͥȃ̹͘ȄΠϋତ͈ܰଷ͉ͬ ̴̳̭͂ͤ͢ͅȄ߿୭͈ࠗুဇഽ̦௩̱̀Ȅݽު၌ף ͬષ̬̭̦ͥ͂خෝ̈́߿ͬࡉ̳̭͂͜خෝ̜́ͥȃ ̭͈̠͢ͅȄGA ͬഐဥུ̱̹ࡄݪ༹͈͉࡛শതȄ ̜̞͉ͥြထ௶ͅܖ̞̿̀ഐ୨̈́࿒എ۾ତ͈ૄ୭ ࣐̠̭ͬ͂ͤ͢ͅȄ̷͈শത̤̫ͥͅडഐ߿͈ထ௶̦ ̧́Ȅ૧̱̞ݽ߿͈ΪϋΠ̢͂̈́ͤͥࡉ̦ංͣͦ ͈ͥ͂͜ܢఞ̧́ͥȃ
Table 4 Principal particulars of the optimum ships and their profi ts calculated by the real-coded GA under the condition of high fuel price Condition of the gross
tonnage constant free
Ship speed [kts] 8 10 12 8 10 12 L [m] 39.90 41.40 42.50 42.20 42.70 42.80 B [m] 6.35 6.44 6.84 7.30 6.95 6.41 d [m] 2.94 3.09 3.06 3.27 2.89 2.59 Cb 0.640 0.578 0.536 0.700 0.620 0.575 Cm 0.930 0.895 0.947 0.945 0.945 0.945 lcb [%] -2.40 -1.40 -1.19 -0.484 -1.59 -1.69 iE [deg.] 27.1 26.4 23.8 23.0 22.1 22.0 Cp 0.688 0.646 0.566 0.741 0.626 0.608 ∆[t] 488.3 488.3 488.3 722.8 545.0 419 Rf [N] 6796 10397 14720 8524 11112 13669 Rw [N] 2353 5363 16443 12389 8416 15027 Rt [N] 9149 15760 31163 20913 19527 28696 BHP [kw] 62 135 321 143 167 295 Total Catch [ton] 2666 2631 2638 4141 3062 2279 Income [106 yen] 186.6 184.2 184.6 289.9 214.4 159.6
Fuel cost [106 yen] 6.8 14.6 34.7 15.5 18.1 32.0
৫ȁৃ ߇ਗఱڠఱڠ֭ࢥڠࡄݪ֭հޗȄݞ͍Ժ२֔௮ ോࡄݪਫ਼५ྶ૾ฎআͤ͢Ȅତ GA ̞̾̀ͅ ࡃ̈́̓ခף̈́ૂ༭̮ͬޗ̧̞̹̺̱̹͘ȃ̭̭ܱ̱ͅ ̀৫փͬນ̱̳͘ȃ̹͘ȄΫΛΠΑΠςϋΈ GA ͥ͢ͅ ࠗॳ͈֚໐̱̩̹ͬ̀ͦު͈߬ࡔםਏ߯ۜͅ৫ ̱̳͘ȃ ȁࡃ 2ȫ କॲࣣࡄݪΓϋΗȜȄକॲࢥڠࡄݪਫ਼Ȫ3113ȫ. ଼ 25 ාഽݽުܿ໐࣒ٛਬȄȶྶ͈ݽ௨ͬࣉ̢ͥȷȅ 3ȫ ༿ఆࢫȄ२׆ંਏȪ3116ȫȅِ̦࣭͈ြ߿ݽ͈৽ါ ࿒ͅ۾̳ͥࡄݪȅུକॲࢥڠٛڠ࣒࣒ٛაਬȄ 276-279ȅ 4ȫ କॲࣣࡄݪΓϋΗȜȄକॲࢥڠࡄݪਫ਼Ȫ3114ȫȅ଼ 26 ාഽݽުܿ໐࣒ٛਬȄȶྶ͈ݽݽުͬࣉ̢ͥȷȅ 5ȫ ܊ུधষȄઐ໌Ȅࣽୌ֚Ȅષၦ௬Ȫ3113ȫȅ५࢛ࡇ ͈૧߿ࣣ̞ؗೲ̧֨࿌ݽ̞̾̀ͅȪల 2 ༭ȫȅୌ໐௮ ٛٛ༭Ȅ215ȇ232-244ȅ 6ȫ କॲࣣࡄݪΓϋΗȜȄକॲࢥڠࡄݪਫ਼Ȫ3113ȫȅȶ̷̠֚ ̧͍ڥ̫٠̱ݽ͈κΟσ୭ࠗै଼ͅ۾̳ͥࡄݪȷ༭࣬ ȅ 7ȫ չೳȪ2::5ȫȅ֒ഥͺσΌςΒθ͈ܖயȝ GA ͈ඨͬ ٜ̩ȝ , Ȝθ২ȅ 8ȫ നၻȄఆଳহࡣȄ५ਘȪ2::8ȫȅΩΕΰڠ ͐֒ഥഎͺσΌςΒθ͈ܖய͂؊ဥȄ૩ཤๅڼٛ২ȅ 9ȫ Ȅ५ఆٗࢨȄܔఉ֚Ȫ3111ȫȅତ GA ̷͈͂؊ဥȅ ૽ࢥෝڠٛধȄ26Ȫ3ȫȇ36:-377ȅ :ȫ ȄऎࢼȄႅਹȪ2:::ȫȅౙ༰ୃܰືए UNDXͬဥ̞̹ତ GA ͥ͢ͅ۾ତडഐاȅ૽ࢥෝڠ ٛধȄ25Ȫ7ȫȇ2257-2266ȅ 21ȫ ऎࢼȄȄႅਹȪ2::8ȫȅ֒ഥഎͺσΌςΒθ ̤̫ͥͅଲయయκΟσ͈մ͂ບثȅ૽ࢥෝڠٛধȄ 23Ȫ6ȫȇ845-855ȅ 22ȫ ᎢհȄ२࿐ํȄזၦࢤȪ3113ȫ֒ഥഎͺσΌς Βθ̤̫ͥͅତα·Πσນ࡛ȄଲయయκΟσȄ༦ ਬ౬ڬ࢘ض͈൦ȅThe Science and Engineering Doshisha UniversityȄXXȪYȫȅ
23ȫ van Oortmerssen, G.Ȫ 2:82ȫȅA Power Prediction Method and Its Application to Small ShipsȅInternational Ship Progress, 29 Ȫ22ȫȇ4:8-526ȅ
24ȫ ݽުฒȪ3112ȫȅႅକॲજอ࣐ȅ
