Introduction to the PSO: Origins

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فهرست عناوین اصلی در این پاورپوینت

● Summary
● Introduction to the PSO: Origins
● Introduction to the PSO: Concept
● Introduction to the PSO: Algorithm – Neighborhood
● Introduction to the PSO: Algorithm – Parameterss
● Introduction to the PSO: Algorithm
● Introduction to the PSO: Algorithm – Parameters
● Introduction to the PSO: Algorithm
● Introduction to the PSO: Algorithm – Example
● Introduction to the PSO: Algorithm Characteristics
● Introduction to the PSO: Different Approaches
● PSO for the BPP:
Problem Formulation
● PSO for the BPP:
Initialization
● PSO for the BPP:
Initialization BLF
● PSO for the BPP:
Algorithm
● PSO for the BPP:
Algorithm
● PSO for the BPP:
Problem Formulation
● PSO for the BPP:
Simulation Results
● PSO for the BPP:
Conclusions

نوع زبان: انگلیسی حجم: 2.42 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 42 صفحه
سطح مطلب: نامشخص پسوند فایل: pptx
گروه موضوعی: زمان استخراج مطلب: 2019/05/16 05:39:52

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عبارات مهم استفاده شده در این مطلب

عبارات مهم استفاده شده در این مطلب

pso, particle, algorithm, introduction, p, position, best, swarm, solution, pbest, example, search,

توجه: این مطلب در تاریخ 2019/05/16 05:39:52 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

https://paginas.fe.up.pt/~mac/ensino/docs/DS20102011/Presentations/PopulationalMetaheuristics/PSO__AndryPinto_InesDomingues_LuisRocha_HugoAlves_SusanaCruz.pptx

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عبارات پرتکرار و مهم در این اسلاید عبارتند از: pso, particle, algorithm, introduction, p, position, best, swarm, solution, pbest, example, search,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

the particle swarm optimization algorithm decision support ۲ ۱ ۲ ۱۱ andry pinto hugo alves inês domingues luís rocha susana cruz summary introduction to particle swarm optimization pso origins concept pso algorithm pso for the bin packing problem bpp problem formulation algorithm simulation results introduction to the pso origins inspired from the nature social behavior and dynamic movements with communications of insects birds and fish ۳ introduction to the pso origins in ۱۹۸۶ craig reynolds described this process in ۳ simple behaviors separation avoid crowding local flockmates alignment move towards the average heading of local flockmates cohesion move toward the average position of local flockmates ۴ introduction to the pso origins application to optimization particle swarm optimization proposed by james kennedy russell eberhart ۱۹۹۵ combines self experiences with social experiences ۵ introduction to the pso concept uses a number of agents particles that constitute a swarm moving around in the search space looking for the best solution each particle in search space adjusts its flying according to its own flying experience as well as the flying experience of other particles introduction to the pso concept collection of flying particles swarm changing solutions search area possible solutions movement towards a promising area to get the global optimum each particle keeps track its best solution personal best pbest the best value of any particle global best gbest introduction to the pso concept each particle adjusts its travelling speed dynamically corresponding to the flying experiences of itself and its colleagues each particle modifies its position according to its current position its current velocity the distance between its current position and pbest the distance between its current position and gbest introduction to the pso algorithm neighborhood geographical social introduction to the pso algorithm neighborhood global introduction to the pso algorithm parameterss algorithm parameters a population of agents pi position of agent ai in the solution space f objective function vi velocity of agent’s ai v ai neighborhood of agent ai fixed the neighborhood concept in pso is not the same as the one used in other meta heuristics search since in pso each particle’s neighborhood never changes is fixed introduction to the pso algorithm x pso p particle initialization for i ۱ to it max for each particle p in p do fp f p if fp is better than f pbest pbest p end end gbest best p in p for each particle p in p do v v c۱ rand pbest – p c۲ rand gbest – p p p v end end introduction to the pso algorithm particle update rule p p v with v v c۱ rand pbest – p c۲ rand gbest – p where p particle’s position v path direction c۱ weight of local information c۲ weight of global information pbest best position of the particle gbest best position of the swarm rand random variable c۱ c۲ the balance factors between the effect of self knowledge and social knowledge in moving the particle towards the target. usually the value ۲ is suggested for both factors in the literature rand a random number between and ۱ and different at each iteration w inertia weight pbest the best position of a particle gbest the best position within the swarm prtvel the velocity of jth particle in ith iteration prtpos the position of jth particle in ith iteration introduction to the pso algorithm parameters number of particles usually between ۱ and ۵ c۱ is the importance of personal best value c۲ is the importance of neighborhood best value usually c۱ c۲ ۴ empirically chosen value if velocity is too low → algorithm too slow if velocity is too high → algorithm too unstable introduction to the pso algorithm create a ‘population’ of agents particles uniformly distributed over x evaluate each particle’s position according to the objective function if a particle’s current position is better than its previous best position update it determine the best particle according to the particle’s previous best positions introduction to the pso algorithm update particles’ velocities move particles to their new positions go to step ۲ until stopping criteria are satisfied introduction to the pso algorithm particle’s velocity makes the particle move in the same direction and with the same velocity ۱. inertia ۲. personal influence ۳. social influence improves the individual makes the particle return to a previous position better than the current conservative makes the particle follow the best neighbors direction introduction to the pso algorithm intensification explores the previous solutions finds the best solution of a given region diversification searches new solutions finds the regions with potentially the best solutions in pso introduction to the pso algorithm example introduction to the pso algorithm example introduction to the pso algorithm example introduction to the pso algorithm example introduction to the pso algorithm example introduction to the pso algorithm example introduction to the pso algorithm example introduction to the pso algorithm example introduction to the pso algorithm characteristics advantages insensitive to scaling of design variables simple implementation easily parallelized for concurrent processing derivative free very few algorithm parameters very efficient global search algorithm disadvantages tendency to a fast and premature convergence in mid optimum points slow convergence in refined search stage weak local search ability introduction to the pso different approaches several approaches ۲ d otsu pso active target pso adaptive pso adaptive mutation pso adaptive pso guided by acceleration information attractive repulsive particle swarm optimization binary pso cooperative multiple pso dynamic and adjustable pso extended particle swarms … davoud sedighizadeh and ellips masehian particle swarm optimization methods taxonomy and applications . international journal of computer theory and engineering vol. ۱ no. ۵ december ۲ ۹ ۲۸ on solving multiobjective bin packing problem using particle swarm optimization d.s liu k.c. tan c.k. …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: pso, particle, algorithm, introduction, p, position, best, swarm, solution, pbest, example, search,

این فایل پاورپوینت شامل 42 اسلاید و به زبان انگلیسی و حجم آن 2.42 مگا بایت است. نوع قالب فایل pptx بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/05/16 05:39:52 استخراج شده است.

https://paginas.fe.up.pt/~mac/ensino/docs/DS20102011/Presentations/PopulationalMetaheuristics/PSO__AndryPinto_InesDomingues_LuisRocha_HugoAlves_SusanaCruz.pptx

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