An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

Abstract

In this paper, an adaptive immune clone selection algorithm for multi-objective optimization (AICSAMO) is proposed. A novel adaptive polynomial mutation operator with dynamic mutation probability is employed in AICSAMO. This adaptive mutation operator executes a rapid global search at the earlier stage of the algorithm and a fine-tuning search at the later stage of the algorithm, which adopts generation-dependent parameters to improve the convergence speed and global optimum searching ability. The effectiveness of AICSAMO is evaluated through the truss sizing and shape optimization problems of a 10-bar plane truss and a 25-bar space truss. According to the comparison of AICSAMO with various multi-objective optimization algorithms developed recently, the simulation results illustrate that AICSAMO has remarkable performance in finding a wider spread of optimal solutions and in maintaining better uniformity of the solutions with better convergence.

Wireless sensor network node deployment based on multi-objective immune algorithm

“…In this section, we introduce the multi-objective immune algorithm (Xie et al, 2016) to solve the node deployment in WSN via sensor rearrangement in the sensing area to remit the coverage holes with limited mobility cost. Multi-objective optimisation refers to the process of simultaneously optimising two or more conflicting objectives subject to some given constraints.…”

Peningkatan Performansi Multi Objektif Nsga-Ii Dengan Operator Mutasi Adaptif Pada Kasus Portofolio Reksadana Saham

“…Namun nilai nilai nm dapat berubah selama proses pencarian solusi disetiap iterasinya hal ini dilakukan untuk mengontrol besarnya nilai mutasi [10]. Begitupun dengan probabilitas mutasi (Pm) yang mengatur peluang banyaknya individu yang mengalami mutasi dalam satu populasi dibuat dinamis setiap iterasinya [11]. Optimasi pembentukan portofolio reksadana saham dengan algoritme multi objektif sebelumnya dilakukan dengan parameter algoritme genetika probabilitas crossover 0.65, probabilitas mutasi 0,05, generasi 400 dan populasi yang berjumlah 20 menghasilkan kombinasi portofolio terbaik untuk periode 24 bulan dengan susunan 20 portofolio [12].…”

“…Semakin besar nilai Pm maka akan semakin banyak gen yang mengalami mutasi pada suatu popolasi. Untuk mengontrol banyaknya gen yang mengalami mutasi dapat dilakukan dengan membuat Pm dinamis [11]. Pm dinamis dilakukan dengan persamaan 7.…”