BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Shanghai BEGIN:STANDARD DTSTART:19910915T010000 TZOFFSETFROM:+0900 TZOFFSETTO:+0800 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260609T092228Z UID:EE7597F7-66AA-45C2-9F6A-E4A32067DD54 DTSTART;TZID=Asia/Shanghai:20260602T150000 DTEND;TZID=Asia/Shanghai:20260602T160000 DESCRIPTION:This event is jointly sponsored by Finland Section Chapter CAS0 4\, Nanjing Section Chapter CH11261\, University of Oulu\, and Hong Kong P olytechnic University.\n\nDue to increased complexity of the systems\, cos t reduction and detail modeling of the systems\, the requirements of optim ization have been increased. The conventional methods\, which guarantee to provide the optimal solution\, fail to solve many practical problems due to several requirements of these methods. The most familiar conventional o ptimization techniques fall in two categories viz. calculus based method a nd enumerative schemes. Though well developed\, these techniques possess s ignificant drawbacks. Calculus based optimization generally relies on cont inuity assumptions and existence of derivatives. Enumerative techniques re ly on special convergence properties and auxiliary function evaluation. Mo reover\, these optimizations are generally single path search and stuck wi th the local optima.\n\nIntelligent based optimization methods such as gen etic algorithm (GA)\, particle warm optimization (PSO)\, bacteria foraging \, ant colony\, neural networks\, etc. are multi-path search and provide s olution near to the global optima. They do not require derivatives of obje ctive function and constraints. This presentation briefly covers some of t he important techniques of optimization along with scope and future challe nges.\n\nSpeaker(s): S. N. Singh\, \n\nVirtual: https://events.vtools.ieee .org/m/561973 LOCATION:Virtual: https://events.vtools.ieee.org/m/561973 ORGANIZER:shiy@yzu.edu.cn SEQUENCE:33 SUMMARY:Intelligent System Applications in Solving Engineering Problems URL;VALUE=URI:https://events.vtools.ieee.org/m/561973 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-bottom: 0c m\; text-align: justify\; text-justify: inter-ideograph\; line-height: nor mal\;"><strong><span lang="EN-US" style="font-size: 12.0pt\; mso-bidi-font -size: 11.0pt\; font-family: 'Times New Roman'\,serif\;">This event is joi ntly sponsored by Finland Section Chapter CAS04\, Nanjing Section Chapter CH11261\, University of Oulu\, and Hong Kong Polytechnic University.</span ></strong></p>\n<p class="MsoNormal" style="margin-bottom: 0cm\; text-alig n: justify\; text-justify: inter-ideograph\; line-height: normal\;"><span lang="EN-US" style="font-size: 12.0pt\; mso-bidi-font-size: 11.0pt\; font- family: 'Times New Roman'\,serif\;">Due to increased complexity of the sys tems\, cost reduction and detail modeling of the systems\, the requirement s of optimization have been increased. The conventional methods\, which gu arantee to provide the optimal solution\, fail to solve many practical pro blems due to several requirements of these methods. </span><span lang="EN- US" style="font-size: 12.0pt\; font-family: 'Times New Roman'\,serif\;">Th e most familiar conventional optimization techniques fall in two categorie s viz. calculus based method and enumerative schemes. Though well develope d\, these techniques possess significant drawbacks. Calculus based optimiz ation generally relies on continuity assumptions and existence of derivati ves. Enumerative techniques rely on special convergence properties and aux iliary function evaluation. Moreover\, these optimizations are generally s ingle path search and stuck with the local optima.&nbsp\;</span></p>\n<p c lass="MsoNormal" style="margin-bottom: 0cm\; text-align: justify\; text-ju stify: inter-ideograph\; line-height: normal\;"><span lang="EN-US" style=" font-size: 12.0pt\; font-family: 'Times New Roman'\,serif\;">Intelligent b ased optimization methods such as genetic algorithm (GA)\, particle warm o ptimization (PSO)\, bacteria foraging\, ant colony\, neural networks\, etc . are multi-path search and provide solution near to the global optima. Th ey do not require derivatives of objective function and constraints. This presentation briefly covers some of the important techniques of optimizati on along with scope and future challenges.</span></p> END:VEVENT END:VCALENDAR