การคำนวณเชิงวิวัฒนาการระหว่าง เจเนติกอัลกอริทึม กับ พาติคอลสวอมออปติมัยเซชัน
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- Genetic algorithm (GA) สามารถแก้ปัญหาและค้นหาผลลัพธ์ได้ดี แต่กระนั้นเจเนติกอัลกอริทึมยังขาดในด้านการแบ่งข้อมูลระหว่างประชากรจึงทำให้ GA ค้นหาผลลัพธ์ค่อนข้างช้า วิธีการใหม่ที่ได้รับการกล่าวถึงคือ Particle Swarm Optimization (PSO) ซึ่งจัดอยู่ในกลุ่มทฤษฎีการวิวัฒนาการกลุ่มเดียวกับ GA โดย PSO ใช้การจำลองหลักการค้นหาอาหารหรือเคลื่อนย้ายของสิ่งมีชีวิต ที่อยู่เป็ นกลุ่ม เช่นฝูงนกหรือฝูงปลา ไม่เหมือนกับ GA ที่ใช้การแข่งขันของประชากรค้นหาผลลัพธ์ บทความนี้นำเสนอเทคนิคของ PSO โดยเปรียบเทียบกับ GA เพื่อเชื่อมโยงความเหมือนและความต่างระหว่าง GA กับ PSO และสามารถนำไปประยุกต์ใช้งานเพื่อแก้ปัญหาที่ซับซ้อนได้
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